Showing papers on "Probabilistic latent semantic analysis published in 2015"
30 Jul 2015
TL;DR: It is shown that the observed features model is most effective at capturing the information present for entity pairs with textual relations, and a combination of the two combines the strengths of both model types.
Abstract: In this paper we show the surprising effectiveness of a simple observed features model in comparison to latent feature models on two benchmark knowledge base completion datasets, FB15K and WN18. We also compare latent and observed feature models on a more challenging dataset derived from FB15K, and additionally coupled with textual mentions from a web-scale corpus. We show that the observed features model is most effective at capturing the information present for entity pairs with textual relations, and a combination of the two combines the strengths of both model types.
808 citations
Proceedings Article•
07 Dec 2015TL;DR: It is argued that through the use of high-level latent random variables, the variational RNN (VRNN)1 can model the kind of variability observed in highly structured sequential data such as natural speech.
Abstract: In this paper, we explore the inclusion of latent random variables into the hidden state of a recurrent neural network (RNN) by combining the elements of the variational autoencoder. We argue that through the use of high-level latent random variables, the variational RNN (VRNN)1 can model the kind of variability observed in highly structured sequential data such as natural speech. We empirically evaluate the proposed model against other related sequential models on four speech datasets and one handwriting dataset. Our results show the important roles that latent random variables can play in the RNN dynamics.
539 citations
17 Oct 2015
TL;DR: A general deep architecture for CF is proposed by integrating matrix factorization with deep feature learning, which leads to a parsimonious fit over the latent features as indicated by its improved performance in comparison to prior state-of-art models over four large datasets for the tasks of movie/book recommendation and response prediction.
Abstract: Collaborative filtering (CF) has been widely employed within recommender systems to solve many real-world problems. Learning effective latent factors plays the most important role in collaborative filtering. Traditional CF methods based upon matrix factorization techniques learn the latent factors from the user-item ratings and suffer from the cold start problem as well as the sparsity problem. Some improved CF methods enrich the priors on the latent factors by incorporating side information as regularization. However, the learned latent factors may not be very effective due to the sparse nature of the ratings and the side information. To tackle this problem, we learn effective latent representations via deep learning. Deep learning models have emerged as very appealing in learning effective representations in many applications. In particular, we propose a general deep architecture for CF by integrating matrix factorization with deep feature learning. We provide a natural instantiations of our architecture by combining probabilistic matrix factorization with marginalized denoising stacked auto-encoders. The combined framework leads to a parsimonious fit over the latent features as indicated by its improved performance in comparison to prior state-of-art models over four large datasets for the tasks of movie/book recommendation and response prediction.
420 citations
TL;DR: This article extended two Dirichlet multinomial topic models by incorporating latent feature vector representations of words trained on very large corpora to improve the word-topic mapping learnt on a smaller corpus.
Abstract: Probabilistic topic models are widely used to discover latent topics in document collections, while latent feature vector representations of words have been used to obtain high performance in many NLP tasks. In this paper, we extend two different Dirichlet multinomial topic models by incorporating latent feature vector representations of words trained on very large corpora to improve the word-topic mapping learnt on a smaller corpus. Experimental results show that by using information from the external corpora, our new models produce significant improvements on topic coherence, document clustering and document classification tasks, especially on datasets with few or short documents.
276 citations
TL;DR: Different models, such as topic over time (TOT), dynamic topic models (DTM), multiscale topic tomography, dynamic topic correlation detection, detecting topic evolution in scientific literature, etc. are discussed.
Abstract: Topic models provide a convenient way to analyze large of unclassified text. A topic contains a cluster of words that frequently occur together. A topic modeling can connect words with similar meanings and distinguish between uses of words with multiple meanings. This paper provides two categories that can be under the field of topic modeling. First one discusses the area of methods of topic modeling, which has four methods that can be considerable under this category. These methods are Latent semantic analysis (LSA), Probabilistic latent semantic analysis (PLSA), Latent Dirichlet allocation (LDA), and Correlated topic model (CTM). The second category is called topic evolution models, which model topics by considering an important factor time. In the second category, different models are discussed, such as topic over time (TOT), dynamic topic models (DTM), multiscale topic tomography, dynamic topic correlation detection, detecting topic evolution in scientific literature, etc.
243 citations
TL;DR: A semantic allocation level (SAL) multifeature fusion strategy based on PTM, namely, SAL-PTM (S AL-pLSA and SAL-LDA) for HSR imagery is proposed, and the experimental results confirmed that SAL- PTM is superior to the single-feature methods and CAT-PTm in the scene classification of H SR imagery.
Abstract: Scene classification has been proved to be an effective method for high spatial resolution (HSR) remote sensing image semantic interpretation. The probabilistic topic model (PTM) has been successfully applied to natural scenes by utilizing a single feature (e.g., the spectral feature); however, it is inadequate for HSR images due to the complex structure of the land-cover classes. Although several studies have investigated techniques that combine multiple features, the different features are usually quantized after simple concatenation (CAT-PTM). Unfortunately, due to the inadequate fusion capacity of $\boldsymbol{k}$ -means clustering, the words of the visual dictionary obtained by CAT-PTM are highly correlated. In this paper, a semantic allocation level (SAL) multifeature fusion strategy based on PTM, namely, SAL-PTM (SAL-pLSA and SAL-LDA) for HSR imagery is proposed. In SAL-PTM: 1) the complementary spectral, texture, and scale-invariant-feature-transform features are effectively combined; 2) the three features are extracted and quantized separately by $\boldsymbol{k}$ -means clustering, which can provide appropriate low-level feature descriptions for the semantic representations; and 3)the latent semantic allocations of the three features are captured separately by PTM, which follows the core idea of PTM-based scene classification. The probabilistic latent semantic analysis (pLSA) and latent Dirichlet allocation (LDA) models were compared to test the effect of different PTMs for HSR imagery. A U.S. Geological Survey data set and the UC Merced data set were utilized to evaluate SAL-PTM in comparison with the conventional methods. The experimental results confirmed that SAL-PTM is superior to the single-feature methods and CAT-PTM in the scene classification of HSR imagery.
217 citations
TL;DR: The R package LSAfun enables a variety of functions and computations based on Vector Semantic Models such as Latent Semantic Analysis (LSA), which are procedures to obtain a high-dimensional vector representation for words (and documents) from a text corpus.
Abstract: In this article, the R package LSAfun is presented. This package enables a variety of functions and computations based on Vector Semantic Models such as Latent Semantic Analysis (LSA) Landauer, Foltz and Laham (Discourse Processes 25:259–284, 1998), which are procedures to obtain a high-dimensional vector representation for words (and documents) from a text corpus. Such representations are thought to capture the semantic meaning of a word (or document) and allow for semantic similarity comparisons between words to be calculated as the cosine of the angle between their associated vectors. LSAfun uses pre-created LSA spaces and provides functions for (a) Similarity Computations between words, word lists, and documents; (b) Neighborhood Computations, such as obtaining a word’s or document’s most similar words, (c) plotting such a neighborhood, as well as similarity structures for any word lists, in a two- or three-dimensional approximation using Multidimensional Scaling, (d) Applied Functions, such as computing the coherence of a text, answering multiple choice questions and producing generic text summaries; and (e) Composition Methods for obtaining vector representations for two-word phrases. The purpose of this package is to allow convenient access to computations based on LSA.
108 citations
01 Jan 2015
TL;DR: Experiments using a state-of-theart LVCSR system showed adaptation could yield perplexity reductions of 8% relatively over the baseline RNNLM and small but consistent word error rate reductions.
Abstract: Copyright © 2015 ISCA. Recurrent neural network language models (RNNLMs) have recently become increasingly popular for many applications including speech recognition. In previous research RNNLMs have normally been trained on well-matched in-domain data. The adaptation of RNNLMs remains an open research area to be explored. In this paper, genre and topic based RNNLMadaptation techniques are investigated for a multi-genre broadcast transcription task. A number of techniques including Probabilistic Latent Semantic Analysis, Latent Dirichlet Allocation and Hierarchical Dirichlet Processes are used to extract show level topic information. These were then used as additional input to the RNNLM during training, which can facilitate unsupervised test time adaptation. Experiments using a state-of-theart LVCSR system trained on 1000 hours of speech and more than 1 billion words of text showed adaptation could yield perplexity reductions of 8% relatively over the baseline RNNLM and small but consistent word error rate reductions.
102 citations
01 Jun 2015
TL;DR: An unsupervised topic model for short texts that performs soft clustering over distributed representations of words using Gaussian mixture models whose components capture the notion of latent topics and which outperforms LDA on short texts through both subjective and objective evaluation.
Abstract: We present an unsupervised topic model for short texts that performs soft clustering over distributed representations of words. We model the low-dimensional semantic vector space represented by the dense distributed representations of words using Gaussian mixture models (GMMs) whose components capture the notion of latent topics. While conventional topic modeling schemes such as probabilistic latent semantic analysis (pLSA) and latent Dirichlet allocation (LDA) need aggregation of short messages to avoid data sparsity in short documents, our framework works on large amounts of raw short texts (billions of words). In contrast with other topic modeling frameworks that use word cooccurrence statistics, our framework uses a vector space model that overcomes the issue of sparse word co-occurrence patterns. We demonstrate that our framework outperforms LDA on short texts through both subjective and objective evaluation. We also show the utility of our framework in learning topics and classifying short texts on Twitter data for English, Spanish, French, Portuguese and Russian.
92 citations
16 Sep 2015
TL;DR: In this paper, a dynamic matrix factorization model based on the Poisson factorization for recommender systems is proposed, which models the time evolving latent factors with a Kalman filter and the actions with Poisson distributions.
Abstract: Models for recommender systems use latent factors to explain the preferences and behaviors of users with respect to a set of items (e.g., movies, books, academic papers). Typically, the latent factors are assumed to be static and, given these factors, the observed pref- erences and behaviors of users are assumed to be generated without order. These assumptions limit the explorative and predictive capabilities of such models, since users' interests and item popularity may evolve over time. To address this, we propose dPF, a dynamic matrix factorization model based on the recent Poisson factorization model for recommendations. dPF models the time evolving latent factors with a Kalman filter and the actions with Poisson distributions. We derive a scalable variational inference algorithm to infer the latent factors. Finally, we demonstrate dPF on 10 years of user click data from arXiv.org, one of the largest repository of scientific papers and a formidable source of information about the behavior of scientists. Empirically we show performance improvement over both static and, more recently proposed, dynamic recommendation models. We also provide a thorough exploration of the inferred posteriors over the latent variables.
92 citations
TL;DR: Light is shed on the theory that underlies text mining methods and guidance is provided for researchers who seek to apply these methods.
Abstract: The amount of textual data that is available for researchers and businesses to analyze is increasing at a dramatic rate. This reality has led IS researchers to investigate various text mining techniques. This essay examines four text mining methods that are frequently used in order to identify their characteristics and limitations. The four methods that we examine are (1) latent semantic analysis, (2) probabilistic latent semantic analysis, (3) latent Dirichlet allocation, and (4) correlated topic model. We review these four methods and compare them with topic detection and spam filtering to reveal their peculiarity. Our paper sheds light on the theory that underlies text mining methods and provides guidance for researchers who seek to apply these methods.
TL;DR: This paper proposes the latent category learning (LCL), an unsupervised learning method which requires only image-level class labels to learn the latent categories, which represent objects, object parts or backgrounds, and achieves the detection precision which outperforms previous results by a large margin.
Abstract: Localizing objects in cluttered backgrounds is challenging under large-scale weakly supervised conditions. Due to the cluttered image condition, objects usually have large ambiguity with backgrounds. Besides, there is also a lack of effective algorithm for large-scale weakly supervised localization in cluttered backgrounds. However, backgrounds contain useful latent information, e.g., the sky in the aeroplane class. If this latent information can be learned, object-background ambiguity can be largely reduced and background can be suppressed effectively. In this paper, we propose the latent category learning (LCL) in large-scale cluttered conditions. LCL is an unsupervised learning method which requires only image-level class labels. First, we use the latent semantic analysis with semantic object representation to learn the latent categories, which represent objects, object parts or backgrounds. Second, to determine which category contains the target object, we propose a category selection strategy by evaluating each category’s discrimination. Finally, we propose the online LCL for use in large-scale conditions. Evaluation on the challenging PASCAL Visual Object Class (VOC) 2007 and the large-scale imagenet large-scale visual recognition challenge 2013 detection data sets shows that the method can improve the annotation precision by 10% over previous methods. More importantly, we achieve the detection precision which outperforms previous results by a large margin and can be competitive to the supervised deformable part model 5.0 baseline on both data sets.
TL;DR: Experimental results over TREC collections show that the proposed LES approach is effective in capturing latent semantic content and can significantly improve the search accuracy of several state-of-the-art retrieval models for entity-bearing queries.
Abstract: Analysis on Web search query logs has revealed that there is a large portion of entity-bearing queries, reflecting the increasing demand of users on retrieving relevant information about entities such as persons, organizations, products, etc. In the meantime, significant progress has been made in Web-scale information extraction, which enables efficient entity extraction from free text. Since an entity is expected to capture the semantic content of documents and queries more accurately than a term, it would be interesting to study whether leveraging the information about entities can improve the retrieval accuracy for entity-bearing queries. In this paper, we propose a novel retrieval approach, i.e., latent entity space (LES), which models the relevance by leveraging entity profiles to represent semantic content of documents and queries. In the LES, each entity corresponds to one dimension, representing one semantic relevance aspect. We propose a formal probabilistic framework to model the relevance in the high-dimensional entity space. Experimental results over TREC collections show that the proposed LES approach is effective in capturing latent semantic content and can significantly improve the search accuracy of several state-of-the-art retrieval models for entity-bearing queries.
09 Aug 2015
TL;DR: This work proposes a new latent approach for binary feedback in CF and shows that even with simple factorization methods like SVD, this approach outperforms existing models and produces state-of-the-art results.
Abstract: In many collaborative filtering (CF) applications, latent approaches are the preferred model choice due to their ability to generate real-time recommendations efficiently. However, the majority of existing latent models are not designed for implicit binary feedback (views, clicks, plays etc.) and perform poorly on data of this type. Developing accurate models from implicit feedback is becoming increasingly important in CF since implicit feedback can often be collected at lower cost and in much larger quantities than explicit preferences. The need for accurate latent models for implicit data was further emphasized by the recently conducted Million Song Dataset Challenge organized by Kaggle. In this challenge, the results for the best latent model were orders of magnitude worse than neighbor-based approaches, and all the top performing teams exclusively used neighbor-based models. We address this problem and propose a new latent approach for binary feedback in CF. In our model, neighborhood similarity information is used to guide latent factorization and derive accurate latent representations. We show that even with simple factorization methods like SVD, our approach outperforms existing models and produces state-of-the-art results.
TL;DR: This article explores a method for modeling associations among binary and ordered categorical variables that has the advantage that maximum-likelihood estimation can be used in multivariate models without numerical integration because the observed data log- likelihood has an explicit form.
Abstract: This article explores a method for modeling associations among binary and ordered categorical variables. The method has the advantage that maximum-likelihood estimation can be used in multivariate models without numerical integration because the observed data log-likelihood has an explicit form. The association model is especially useful with mixture models to handle violations of the local independence assumption. Applications to latent class and latent transition analysis are presented.
TL;DR: This work presents a novel automatic change message classification method characterized by semi-supervised topic semantic analysis that automatically classifies most of the change messages which record the cause of the software change and is applicable to cross-project analysis of software change messages.
Abstract: Context Topic models such as probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA) have demonstrated success in mining software repository tasks. Understanding software change messages described by the unstructured nature-language text is one of the fundamental challenges in mining these messages in repositories. Objective We seek to present a novel automatic change message classification method characterized by semi-supervised topic semantic analysis. Method In this work, we present a semi-supervised LDA based approach to automatically classify change messages. We use domain knowledge of software changes to make labeled samples which are added to build the semi-supervised LDA model. Next, we verify the cross-project analysis application of our method on three open-source projects. Our method has two advantages over existing software change classification methods: First of all, it mitigates the issue of how to set the appropriate number of latent topics. We do not have to choose the number of latent topics in our method, because it corresponds to the number of class labels. Second, this approach utilizes the information provided by the label samples in the training set. Results Our method automatically classified about 85% of the change messages in our experiment and our validation survey showed that 70.56% of the time our automatic classification results were in agreement with developer opinions. Conclusion Our approach automatically classifies most of the change messages which record the cause of the software change and the method is applicable to cross-project analysis of software change messages.
TL;DR: A systematic comparison of two extrapolation techniques: k-nearest neighbours, and random forest, in combination with semantic spaces built using latent semantic analysis, topic model, a hyperspace analogue to language (HAL)-like model, and a skip-gram model finds that at least some of the extrapolation methods may introduce artefacts to the data and produce results that could lead to different conclusions that would be reached based on the human ratings.
Abstract: Subjective ratings for age of acquisition, concreteness, affective valence, and many other variables are an important element of psycholinguistic research. However, even for well-studied languages, ratings usually cover just a small part of the vocabulary. A possible solution involves using corpora to build a semantic similarity space and to apply machine learning techniques to extrapolate existing ratings to previously unrated words. We conduct a systematic comparison of two extrapolation techniques: k-nearest neighbours, and random forest, in combination with semantic spaces built using latent semantic analysis, topic model, a hyperspace analogue to language (HAL)-like model, and a skip-gram model. A variant of the k-nearest neighbours method used with skip-gram word vectors gives the most accurate predictions but the random forest method has an advantage of being able to easily incorporate additional predictors. We evaluate the usefulness of the methods by exploring how much of the human performance in...
TL;DR: The newly created NER system is fully language-independent thanks to the unsupervised nature of the proposed features and achieves the same or even better results than state-of-the-art language dependent systems.
Abstract: Language independent Named Entity Recognition system.Novel features based on latent semantics.Experiments on multiple languages - English, Spanish, Dutch, Czech.State-of-the-art results. In this paper, we propose new features for Named Entity Recognition (NER) based on latent semantics. Furthermore, we explore the effect of unsupervised morphological information on these methods and on the NER system in general. The newly created NER system is fully language-independent thanks to the unsupervised nature of the proposed features. We evaluate the system on English, Spanish, Dutch and Czech corpora and study the difference between weakly and highly inflectional languages. Our system achieves the same or even better results than state-of-the-art language dependent systems. The proposed features proved to be very useful and are the main reason of our promising results.
TL;DR: To address the shortcomings of limited research in forecasting the power of social media in India, sentiment analysis and prediction algorithms are used to analyze the performance of Indian movies based on data obtained from social media sites.
Abstract: Purpose – The purpose of this paper is to address the shortcomings of limited research in forecasting the power of social media in India. Design/methodology/approach – This paper uses sentiment analysis and prediction algorithms to analyze the performance of Indian movies based on data obtained from social media sites. The authors used Twitter4j Java API for extracting the tweets through authenticating connection with Twitter web sites and stored the extracted data in MySQL database and used the data for sentiment analysis. To perform sentiment analysis of Twitter data, the Probabilistic Latent Semantic Analysis classification model is used to find the sentiment score in the form of positive, negative and neutral. The data mining algorithm Fuzzy Inference System is used to implement sentiment analysis and predict movie performance that is classified into three categories: hit, flop and average. Findings – In this study the authors found results of movie performance at the box office, which had been based ...
09 Nov 2015
TL;DR: In this paper, deep CNN features and topic features are utilized as visual and textual semantic representation respectively and a regularized deep neural network (RE-DNN) is proposed for semantic mapping across modalities.
Abstract: Cross-Modal mapping plays an essential role in multimedia information retrieval systems. However, most of existing work paid much attention on learning mapping functions but neglected the exploration of high-level semantic representation of modalities. Inspired by recent success of deep learning, in this paper, deep CNN (convolutional neural networks) features and topic features are utilized as visual and textual semantic representation respectively. To investigate the highly non-linear semantic correlation between image and text, we propose a regularized deep neural network(RE-DNN) for semantic mapping across modalities. By imposing intra-modal regularization as supervised pre-training, we finally learn a joint model which captures both intra-modal and inter-modal relationships. Our approach is superior to previous work in follows: (1) it explores high-level semantic correlations, (2) it requires little prior knowledge for model training, (3) it is able to tackle modality missing problem. Extensive experiments on benchmark Wikipedia dataset show RE-DNN outperforms the state-of-the-art approaches in cross-modal retrieval.
TL;DR: A novel method is developed to explicitly map concepts and image contents into a unified latent semantic space for the representation of semantic concept prototypes, such that each image is closer to its relevant concept prototype than other prototypes.
Abstract: This paper introduces a novel approach to facilitating image search based on a compact semantic embedding A novel method is developed to explicitly map concepts and image contents into a unified latent semantic space for the representation of semantic concept prototypes Then, a linear embedding matrix is learned that maps images into the semantic space, such that each image is closer to its relevant concept prototype than other prototypes In our approach, the semantic concepts equated with query keywords and the images mapped into the vicinity of the prototype are retrieved by our scheme In addition, a computationally efficient method is introduced to incorporate new semantic concept prototypes into the semantic space by updating the embedding matrix This novelty improves the scalability of the method and allows it to be applied to dynamic image repositories Therefore, the proposed approach not only narrows semantic gap but also supports an efficient image search process We have carried out extensive experiments on various cross-modality image search tasks over three widely-used benchmark image datasets Results demonstrate the superior effectiveness, efficiency, and scalability of our proposed approach
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TL;DR: A gamma process dynamic Poisson factor analysis model is proposed to factorize a dynamic count matrix, whose columns are sequentially observed count vectors, and applies to text and music analysis, with state-of-the-art results.
Abstract: A gamma process dynamic Poisson factor analysis model is proposed to factorize a dynamic count matrix, whose columns are sequentially observed count vectors. The model builds a novel Markov chain that sends the latent gamma random variables at time $(t-1)$ as the shape parameters of those at time $t$, which are linked to observed or latent counts under the Poisson likelihood. The significant challenge of inferring the gamma shape parameters is fully addressed, using unique data augmentation and marginalization techniques for the negative binomial distribution. The same nonparametric Bayesian model also applies to the factorization of a dynamic binary matrix, via a Bernoulli-Poisson link that connects a binary observation to a latent count, with closed-form conditional posteriors for the latent counts and efficient computation for sparse observations. We apply the model to text and music analysis, with state-of-the-art results.
TL;DR: A novel probabilistic technique, time-delay gaussian-process factor analysis (TD-GPFA), that performs dimensionality reduction in the presence of a different time delay between each pair of latent and observed variables is introduced.
Abstract: Noisy, high-dimensional time series observations can often be described by a set of low-dimensional latent variables. Commonly used methods to extract these latent variables typically assume instantaneous relationships between the latent and observed variables. In many physical systems, changes in the latent variables manifest as changes in the observed variables after time delays. Techniques that do not account for these delays can recover a larger number of latent variables than are present in the system, thereby making the latent representation more difficult to interpret. In this work, we introduce a novel probabilistic technique, time-delay gaussian-process factor analysis TD-GPFA, that performs dimensionality reduction in the presence of a different time delay between each pair of latent and observed variables. We demonstrate how using a gaussian process to model the evolution of each latent variable allows us to tractably learn these delays over a continuous domain. Additionally, we show how TD-GPFA combines temporal smoothing and dimensionality reduction into a common probabilistic framework. We present an expectation/conditional maximization either ECME algorithm to learn the model parameters. Our simulations demonstrate that when time delays are present, TD-GPFA is able to correctly identify these delays and recover the latent space. We then applied TD-GPFA to the activity of tens of neurons recorded simultaneously in the macaque motor cortex during a reaching task. TD-GPFA is able to better describe the neural activity using a more parsimonious latent space than GPFA, a method that has been used to interpret motor cortex data but does not account for time delays. More broadly, TD-GPFA can help to unravel the mechanisms underlying high-dimensional time series data by taking into account physical delays in the system.
TL;DR: The domain of Sports is considered for creating both Low-level visual ontology for certain sport event images and also for building a high-level domain ontology from the information on web that is integrated using Fuzzy concepts.
13 Oct 2015
TL;DR: An on-line semantic coding model, which simultaneously exploits the rich hierarchical semantic prior knowledge in the learned dictionary, reflects semantic sparse property of visual codes, and explores semantic relationships among concepts in the semantic hierarchy is devised.
Abstract: In recent years, tremendous research endeavours have been dedicated to seeking effective visual representations for facilitating various multimedia applications, such as visual annotation and retrieval. Nonetheless, existing approaches can hardly achieve satisfactory performance due to the scarcity of fully exploring semantic properties of visual codes. In this paper, we present a novel visual coding approach, termed as hierarchical semantic visual coding (HSVC), to effectively encode visual objects (e.g., image and video) in a semantic hierarchy. Specifically, we first construct a semantic-enriched dictionary hierarchy, which is comprised of dictionaries corresponding to all concepts in a semantic hierarchy as well as their hierarchical semantic correlation. Moreover, we devise an on-line semantic coding model, which simultaneously 1) exploits the rich hierarchical semantic prior knowledge in the learned dictionary, 2) reflects semantic sparse property of visual codes, and 3) explores semantic relationships among concepts in the semantic hierarchy. To this end, we propose to integrate concept-level group sparsity constraint and semantic correlation matrix into a unified regularization term. We design an effective algorithm to optimize the proposed model, and a rigorous mathematical analysis has been provided to guarantee that the algorithm converges to a global optima. Extensive experiments on various multimedia datasets have been conducted to illustrate the superiority of our proposed approach as compared to state-of-the-art methods.
TL;DR: A new approach based on text mining techniques for predicting student performance using LSA (latent semantic analysis) and K-means clustering methods using free-style comments written by students after each lesson is proposed.
Abstract: In this paper we propose a new approach based on text mining techniques for predicting student performance using LSA (latent semantic analysis) and K-means clustering methods. The present study uses free-style comments written by students after each lesson. Since the potentials of these comments can reflect student learning attitudes, understanding of subjects and difficulties of the lessons, they enable teachers to grasp the tendencies of student learning activities. To improve our basic approach using LSA and k-means, overlap and similarity measuring methods are proposed. We conducted experiments to validate our proposed methods. The experimental results reported a model of student academic performance predictors by analyzing their comments data as variables of predictors. Our proposed methods achieved an average 66.4% prediction accuracy after applying the k-means clustering method and those were 73.6% and 78.5% by adding the overlap method and the similarity measuring method, respectively.
TL;DR: The experimental results demonstrate that the proposed MPLSA method can achieve better scene classification accuracy than the traditional single-feature PLSA method.
Abstract: Scene classification can mine high-level semantic information scene categories from low-level visual features for high spatial resolution remote sensing images (HSRIs). A multifeature probabilistic latent semantic analysis (MPLSA) algorithm is proposed to perform the task of scene classification for HSRIs. Distinct from the traditional probabilistic latent semantic analysis (PLSA) with a single feature, to utilize the spatial information of the HSRIs, in MPLSA, multiple features, including spectral and texture features, and the scale-invariant feature transform feature, are combined with PLSA. The visual words are characterized by the multifeature descriptor, and an image set is represented by a discriminative word-image matrix. During the training phase, the MPLSA model mines the visual words’ latent semantics. For unknown images, the MPLSA model analyzes their corresponding latent semantic distributions by combining the words’ latent semantics obtained from the training step. The spectral angle mapper classifier is utilized to label the scene class, based on the image’s latent semantic distribution. The experimental results demonstrate that the proposed MPLSA method can achieve better scene classification accuracy than the traditional single-feature PLSA method.
TL;DR: Out of the three considered methods, the Semantic IMproved Latent Semantic Analysis is the one that provides better results, when coupled with a proper weighting policy, demonstrating to actually be a helpful tool in supporting scientists in the curation process of gene functional annotations.
Abstract: Gene function annotations, which are associations between a gene and a term of a controlled vocabulary describing gene functional features, are of paramount importance in modern biology. Datasets of these annotations, such as the ones provided by the Gene Ontology Consortium, are used to design novel biological experiments and interpret their results. Despite their importance, these sources of information have some known issues. They are incomplete, since biological knowledge is far from being definitive and it rapidly evolves, and some erroneous annotations may be present. Since the curation process of novel annotations is a costly procedure, both in economical and time terms, computational tools that can reliably predict likely annotations, and thus quicken the discovery of new gene annotations, are very useful. We used a set of computational algorithms and weighting schemes to infer novel gene annotations from a set of known ones. We used the latent semantic analysis approach, implementing two popular algorithms (Latent Semantic Indexing and Probabilistic Latent Semantic Analysis) and propose a novel method, the Semantic IMproved Latent Semantic Analysis, which adds a clustering step on the set of considered genes. Furthermore, we propose the improvement of these algorithms by weighting the annotations in the input set. We tested our methods and their weighted variants on the Gene Ontology annotation sets of three model organism genes (Bos taurus, Danio rerio and Drosophila melanogaster ). The methods showed their ability in predicting novel gene annotations and the weighting procedures demonstrated to lead to a valuable improvement, although the obtained results vary according to the dimension of the input annotation set and the considered algorithm. Out of the three considered methods, the Semantic IMproved Latent Semantic Analysis is the one that provides better results. In particular, when coupled with a proper weighting policy, it is able to predict a significant number of novel annotations, demonstrating to actually be a helpful tool in supporting scientists in the curation process of gene functional annotations.
TL;DR: In this scheme, the mechanical design knowledge ontology is first constructed, and query semantic extension and retrieval are then adopted for knowledge retrieval, and the results demonstrate that the method outperforms the keyword-based search technique.
Abstract: The semantic retrieval of mechanical domain knowledge is critical in product design process. To address the problems with existing keyword-based and semantic-enabled methods, an ontology-based search system (OBSS) for knowledge search and retrieval from knowledge base is proposed. In our scheme, the mechanical design knowledge ontology is first constructed, and query semantic extension and retrieval are then adopted for knowledge retrieval. For the query semantic extension, the semantic similarity analysis is adopted to discover the semantic distance between the semantic key in query and its extended semantic keys. Then, the query boosts of extended semantic keys are modified by the semantic similarity. In the precision and recall experiments, the results demonstrate that our method outperforms the keyword-based search technique. Further, two query cases ensure that the semantic retrieval algorithm based on domain ontology could understand the latent search intents. This research contributes to the query ...
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TL;DR: A novel and fast traffic sign recognition system, a very important part for advanced driver assistance system and for autonomous driving and a challenge to existing state of the art techniques.
Abstract: In this work we developed a novel and fast traffic sign recognition system, a very important part for advanced driver assistance system and for autonomous driving. Traffic signs play a very vital role in safe driving and avoiding accident. We have used image processing and topic discovery model pLSA to tackle this challenging multiclass classification problem. Our algorithm is consist of two parts, shape classification and sign classification for improved accuracy. For processing and representation of image we have used bag of features model with SIFT local descriptor. Where a visual vocabulary of size 300 words are formed using k-means codebook formation algorithm. We exploited the concept that every image is a collection of visual topics and images having same topics will belong to same category. Our algorithm is tested on German traffic sign recognition benchmark (GTSRB) and gives very promising result near to existing state of the art techniques.