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Journal ArticleDOI

Latent dirichlet allocation

TL;DR: This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.
Abstract: We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model.

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Citations
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Proceedings Article
01 Dec 2004
TL;DR: This work presents a novel method for learning with Gaussian process regression in a hierarchical Bayesian framework, and evaluates the approach as a recommendation engine for art images, where the proposed hierarchicalBayesian method leads to excellent prediction performance.
Abstract: We present a novel method for learning with Gaussian process regression in a hierarchical Bayesian framework In a first step, kernel matrices on a fixed set of input points are learned from data using a simple and efficient EM algorithm This step is nonparametric, in that it does not require a parametric form of covariance function In a second step, kernel functions are fitted to approximate the learned covariance matrix using a generalized Nystrom method, which results in a complex, data driven kernel We evaluate our approach as a recommendation engine for art images, where the proposed hierarchical Bayesian method leads to excellent prediction performance

200 citations


Cites background from "Latent dirichlet allocation"

  • ...Recent examples of HB modelling in machine learning include [1, 2 ]. In other contexts, this learning framework is called multi-task learning [4]....

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Journal ArticleDOI
TL;DR: This paper organizes and surveys the corresponding literature, defines unambiguous key terms, and discusses links among fundamental building blocks ranging from human detection to action and interaction recognition, providing a comprehensive coverage of key aspects of video-based human behavior understanding.
Abstract: Understanding human behaviors is a challenging problem in computer vision that has recently seen important advances. Human behavior understanding combines image and signal processing, feature extraction, machine learning, and 3-D geometry. Application scenarios range from surveillance to indexing and retrieval, from patient care to industrial safety and sports analysis. Given the broad set of techniques used in video-based behavior understanding and the fast progress in this area, in this paper we organize and survey the corresponding literature, define unambiguous key terms, and discuss links among fundamental building blocks ranging from human detection to action and interaction recognition. The advantages and the drawbacks of the methods are critically discussed, providing a comprehensive coverage of key aspects of video-based human behavior understanding, available datasets for experimentation and comparisons, and important open research issues.

199 citations


Cites methods from "Latent dirichlet allocation"

  • ...As a classifier for the activity recognition, the authors employ the latent Dirichlet allocation [165] combined with Gibbs sampling....

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Proceedings ArticleDOI
13 Jun 2010
TL;DR: The proposed association model shows improved performance over correspondence LDA as measured by caption perplexity, and a novel latent variable regression approach to capture correlations between image or video features and annotation texts.
Abstract: We present topic-regression multi-modal Latent Dirich-let Allocation (tr-mmLDA), a novel statistical topic model for the task of image and video annotation. At the heart of our new annotation model lies a novel latent variable regression approach to capture correlations between image or video features and annotation texts. Instead of sharing a set of latent topics between the 2 data modalities as in the formulation of correspondence LDA in [2], our approach introduces a regression module to correlate the 2 sets of topics, which captures more general forms of association and allows the number of topics in the 2 data modalities to be different. We demonstrate the power of tr-mmLDA on 2 standard annotation datasets: a 5000-image subset of COREL and a 2687-image LabelMe dataset. The proposed association model shows improved performance over correspondence LDA as measured by caption perplexity.

199 citations

Proceedings ArticleDOI
24 Jul 2011
TL;DR: This paper introduces Interdependent Latent Dirichlet Allocation (ILDA) model, a probabilistic graphical models which aim to extract aspects and corresponding ratings of products from online reviews and conducts experiments on a real life dataset, Epinions.com.
Abstract: Today, more and more product reviews become available on the Internet, e.g., product review forums, discussion groups, and Blogs. However, it is almost impossible for a customer to read all of the different and possibly even contradictory opinions and make an informed decision. Therefore, mining online reviews (opinion mining) has emerged as an interesting new research direction. Extracting aspects and the corresponding ratings is an important challenge in opinion mining. An aspect is an attribute or component of a product, e.g. 'screen' for a digital camera. It is common that reviewers use different words to describe an aspect (e.g. 'LCD', 'display', 'screen'). A rating is an intended interpretation of the user satisfaction in terms of numerical values. Reviewers usually express the rating of an aspect by a set of sentiments, e.g. 'blurry screen'. In this paper we present three probabilistic graphical models which aim to extract aspects and corresponding ratings of products from online reviews. The first two models extend standard PLSI and LDA to generate a rated aspect summary of product reviews. As our main contribution, we introduce Interdependent Latent Dirichlet Allocation (ILDA) model. This model is more natural for our task since the underlying probabilistic assumptions (interdependency between aspects and ratings) are appropriate for our problem domain. We conduct experiments on a real life dataset, Epinions.com, demonstrating the improved effectiveness of the ILDA model in terms of the likelihood of a held-out test set, and the accuracy of aspects and aspect ratings.

198 citations


Cites background or methods from "Latent dirichlet allocation"

  • ...Although the posterior distribution is intractable for exact inference, a wide variety of approximate inference algorithm can be considered for LDA [3]....

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  • ...1) by using a latent random variable θ rather than a large set of individual parameters which are explicitly linked to the training reviews [3]....

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  • ...One reasonable approach to avoid overfitting is assigning probability to previously unseen data by marginalizing over seen data [3]....

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  • ...Then we extend the most well-known method for unsupervised modeling of documents, Latent Dirichlet Allocation (LDA) [3] to solve the considered problem....

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  • ...For this reason, PLSI is not a well-defined generative model of reviews [3]....

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Journal ArticleDOI
TL;DR: A structural topic modeling analysis of 3963 articles published in Computers & Education between 1976 and 2018 bibliometrically provided useful insights and implications, and could be used as a guide for contributors to Computers and Education.
Abstract: Computers & Education has been leading the field of computers in education for over 40 years, during which time it has developed into a well-known journal with significant influences on the educational technology research community. Questions such as “in what research topics were the academic community of Computers & Education interested?” “how did such research topics evolve over time?” and “what were the main research concerns of its major contributors?” are important to both the editorial board and readership of Computers & Education. To address these issues, this paper conducted a structural topic modeling analysis of 3963 articles published in Computers & Education between 1976 and 2018 bibliometrically. A structural topic model was used to profile the research hotspots. By further exploring annual topic proportion trends and topic correlations, potential future research directions and inter-topic research areas were identified. The major research concerns of the publications in Computers & Education by prolific countries/regions were shown and compared. Thus, this work provided useful insights and implications, and it could be used as a guide for contributors to Computers & Education.

198 citations

References
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Book
01 Jan 1995
TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
Abstract: FUNDAMENTALS OF BAYESIAN INFERENCE Probability and Inference Single-Parameter Models Introduction to Multiparameter Models Asymptotics and Connections to Non-Bayesian Approaches Hierarchical Models FUNDAMENTALS OF BAYESIAN DATA ANALYSIS Model Checking Evaluating, Comparing, and Expanding Models Modeling Accounting for Data Collection Decision Analysis ADVANCED COMPUTATION Introduction to Bayesian Computation Basics of Markov Chain Simulation Computationally Efficient Markov Chain Simulation Modal and Distributional Approximations REGRESSION MODELS Introduction to Regression Models Hierarchical Linear Models Generalized Linear Models Models for Robust Inference Models for Missing Data NONLINEAR AND NONPARAMETRIC MODELS Parametric Nonlinear Models Basic Function Models Gaussian Process Models Finite Mixture Models Dirichlet Process Models APPENDICES A: Standard Probability Distributions B: Outline of Proofs of Asymptotic Theorems C: Computation in R and Stan Bibliographic Notes and Exercises appear at the end of each chapter.

16,079 citations


"Latent dirichlet allocation" refers background in this paper

  • ...Finally, Griffiths and Steyvers (2002) have presented a Markov chain Monte Carlo algorithm for LDA....

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  • ...Structures similar to that shown in Figure 1 are often studied in Bayesian statistical modeling, where they are referred to ashierarchical models(Gelman et al., 1995), or more precisely asconditionally independent hierarchical models(Kass and Steffey, 1989)....

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  • ...Structures similar to that shown in Figure 1 are often studied in Bayesian statistical modeling, where they are referred to as hierarchical models (Gelman et al., 1995), or more precisely as conditionally independent hierarchical models (Kass and Steffey, 1989)....

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Journal ArticleDOI
TL;DR: A new method for automatic indexing and retrieval to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries.
Abstract: A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries. The particular technique used is singular-value decomposition, in which a large term by document matrix is decomposed into a set of ca. 100 orthogonal factors from which the original matrix can be approximated by linear combination. Documents are represented by ca. 100 item vectors of factor weights. Queries are represented as pseudo-document vectors formed from weighted combinations of terms, and documents with supra-threshold cosine values are returned. initial tests find this completely automatic method for retrieval to be promising.

12,443 citations


"Latent dirichlet allocation" refers methods in this paper

  • ...To address these shortcomings, IR researchers have proposed several other dimensionality reduction techniques, most notably latent semantic indexing (LSI) (Deerwester et al., 1990)....

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  • ...To address these shortcomings, IR researchers have proposed several other dimensionality reduction techniques, most notablylatent semantic indexing (LSI)(Deerwester et al., 1990)....

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Book
01 Jan 1983
TL;DR: Reading is a need and a hobby at once and this condition is the on that will make you feel that you must read.
Abstract: Some people may be laughing when looking at you reading in your spare time. Some may be admired of you. And some may want be like you who have reading hobby. What about your own feel? Have you felt right? Reading is a need and a hobby at once. This condition is the on that will make you feel that you must read. If you know are looking for the book enPDFd introduction to modern information retrieval as the choice of reading, you can find here.

12,059 citations


"Latent dirichlet allocation" refers background or methods in this paper

  • ...In the populartf-idf scheme (Salton and McGill, 1983), a basic vocabulary of “words” or “terms” is chosen, and, for each document in the corpus, a count is formed of the number of occurrences of each word....

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  • ...We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model....

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Book
01 Jan 1939
TL;DR: In this paper, the authors introduce the concept of direct probabilities, approximate methods and simplifications, and significant importance tests for various complications, including one new parameter, and various complications for frequency definitions and direct methods.
Abstract: 1. Fundamental notions 2. Direct probabilities 3. Estimation problems 4. Approximate methods and simplifications 5. Significance tests: one new parameter 6. Significance tests: various complications 7. Frequency definitions and direct methods 8. General questions

7,086 citations