Topic
Probabilistic latent semantic analysis
About: Probabilistic latent semantic analysis is a research topic. Over the lifetime, 2884 publications have been published within this topic receiving 198341 citations. The topic is also known as: PLSA.
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TL;DR: The theoretical background of LVMMs is provided and their exploratory character is emphasized, the general framework together with assumptions and necessary constraints is outlined, the difference between models with and without covariates is highlighted, and the interrelation between the number of classes and the complexity of the within-class model is discussed as well as the relevance of measurement invariance.
31 citations
TL;DR: This paper presents a novel Content-Based Video Retrieval approach in order to cope with the semantic gap challenge by means of latent topics and reveals that the proposed ranking function is able to provide a competitive advantage within the content-based retrieval field.
31 citations
01 Jul 2017
TL;DR: A Context-Aware POI Category Prediction (CAP-CP) scheme using Natural Language Processing (NLP) models and a novel Temporal Adaptive Ngram (TA-Ngram) model to capture the dynamic dependency between check-in points to address temporal dependency challenge.
Abstract: Point-of-Interest (POI) recommendation is an important application in Location-based Social Networks (LBSN). The category prediction problem is to predict the next POI category that users may visit. The predicted category information is critical in large-scale POI recommendation because it can significantly reduce the prediction space and improve the recommendation accuracy. While efforts have been made to address the POI category prediction problem, several important challenges still exist. First, existing solutions did not fully explore the temporal dependency (e.g., “long range dependency”) of users' check-in traces. Second, the hidden contextual information associated with each check-in point has been underutilized. In this work, we propose a Context-Aware POI Category Prediction (CAP-CP) scheme using Natural Language Processing (NLP) models. In particular, to address temporal dependency challenge, we develop a novel Temporal Adaptive Ngram (TA-Ngram) model to capture the dynamic dependency between check-in points. To address the challenge of hidden context incorporation, CAP-CP leverages the Probabilistic Latent Semantic Analysis (PLSA) model to infer the semantic implications of the context variables in the prediction model. Empirical results on a real world dataset show that our scheme can effectively improve the performance of the state-of-the-art POI recommendation solutions.
31 citations
13 Jul 2006
TL;DR: A novel technique, inspired by a text-retrieval technique known as cross-language latent semantic indexing uses linear algebra to learn the semantic structure linking image features and keywords from a training set of annotated images, thus providing the ability to search the unannotated images based on keyword.
Abstract: This paper presents a novel technique for learning the underlying structure that links visual observations with semantics. The technique, inspired by a text-retrieval technique known as cross-language latent semantic indexing uses linear algebra to learn the semantic structure linking image features and keywords from a training set of annotated images. This structure can then be applied to unannotated images, thus providing the ability to search the unannotated images based on keyword. This factorisation approach is shown to perform well, even when using only simple global image features.
31 citations
01 Jan 2009
TL;DR: The probabilistic latent semantic thesaurus (PLST) is introduced; an efficient and effective method of storing the PLSA information and is able to maintain the high precision results found using PLSA while using a very small percent of the storage space of PLSI.
Abstract: Probabilistic latent semantic analysis (PLSA) is a method for computing term and document relationships from a document set. The probabilistic latent semantic index (PLSI) has been used to store PLSA information, but unfortunately the PLSI uses excessive storage space relative to a simple term frequency index, which causes lengthy query times. To overcome the storage and speed problems of PLSI, we introduce the probabilistic latent semantic thesaurus (PLST); an efficient and effective method of storing the PLSA information. We show that through methods such as document thresholding and term pruning, we are able to maintain the high precision results found using PLSA while using a very small percent (0.15%) of the storage space of PLSI.
31 citations