Latent dirichlet allocation

doi:10.5555/944919.944937

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Latent dirichlet allocation

Journal Article•DOI•

Latent dirichlet allocation

David M. Blei¹, Andrew Y. Ng², Michael I. Jordan¹•Institutions (2)

University of California, Berkeley¹, Stanford University²

01 Mar 2003-Journal of Machine Learning Research (JMLR.org)-Vol. 3, pp 993-1022

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.

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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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Proceedings Article•DOI•

Unsupervised Activity Perception by Hierarchical Bayesian Models

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Xiaogang Wang¹, Xiaoxu Ma¹, Eric Grimson¹•Institutions (1)

Massachusetts Institute of Technology¹

17 Jun 2007

TL;DR: A novel unsupervised learning framework for activity perception to understand activities in complicated scenes from visual data using a hierarchical Bayesian model to connect three elements: low-level visual features, simple "atomic" activities, and multi-agent interactions.

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Abstract: We propose a novel unsupervised learning framework for activity perception. To understand activities in complicated scenes from visual data, we propose a hierarchical Bayesian model to connect three elements: low-level visual features, simple "atomic" activities, and multi-agent interactions. Atomic activities are modeled as distributions over low-level visual features, and interactions are modeled as distributions over atomic activities. Our models improve existing language models such as latent Dirichlet allocation (LDA) and hierarchical Dirichlet process (HDP) by modeling interactions without supervision. Our data sets are challenging video sequences from crowded traffic scenes with many kinds of activities co-occurring. Our approach provides a summary of typical atomic activities and interactions in the scene. Unusual activities and interactions are found, with natural probabilistic explanations. Our method supports flexible high-level queries on activities and interactions using atomic activities as components.

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350 citations

Cites background or methods from "Latent dirichlet allocation"

...Our method supports flexible high-level queries on activities and interactions using atomic activities as components....
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...It also involves many challenging problems, such as lighting changes, occlusions, a variety of object types, object view changes and environmental effects....
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...LDA and HDP are hierarchical Bayesian models for language processing....
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...Our goal is to infer the set of activities (or topics) from video by learning the distributions of features that co-occur, and to learn distributions of activities that co-occur, thus finding interactions....
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Journal Article•DOI•

A Discriminative Kernel-Based Approach to Rank Images from Text Queries

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David Grangier¹, Samy Bengio²•Institutions (2)

Idiap Research Institute¹, Google²

01 Aug 2008-IEEE Transactions on Pattern Analysis and Machine Intelligence

TL;DR: This paper introduces a discriminative model for the retrieval of images from text queries that formalizes the retrieval task as a ranking problem, and introduces a learning procedure optimizing a criterion related to the ranking performance.

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Abstract: This paper introduces a discriminative model for the retrieval of images from text queries. Our approach formalizes the retrieval task as a ranking problem, and introduces a learning procedure optimizing a criterion related to the ranking performance. The proposed model hence addresses the retrieval problem directly and does not rely on an intermediate image annotation task, which contrasts with previous research. Moreover, our learning procedure builds upon recent work on the online learning of kernel-based classifiers. This yields an efficient, scalable algorithm, which can benefit from recent kernels developed for image comparison. The experiments performed over stock photography data show the advantage of our discriminative ranking approach over state-of-the-art alternatives (e.g. our model yields 26.3% average precision over the Corel dataset, which should be compared to 22.0%, for the best alternative model evaluated). Further analysis of the results shows that our model is especially advantageous over difficult queries such as queries with few relevant pictures or multiple-word queries.

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349 citations

Cites methods from "Latent dirichlet allocation"

...A longer survey could also have described alternative models such as the Multimodal Hierarchical Aspect Model [4], [3], the Multiple Bernoulli Relevance Model [16], or Latent Dirichlet Allocation [ 5 ]....
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Journal Article•DOI•

History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining

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Dazhi Yang¹, Jan Kleissl², Christian A. Gueymard, Hugo T.C. Pedro², Carlos F.M. Coimbra² - Show less +1 more•Institutions (2)

Agency for Science, Technology and Research¹, University of California, San Diego²

01 Jul 2018-Solar Energy

TL;DR: This paper presents a preliminary study on how to review solar irradiance and photovoltaic power forecasting using text mining, which serves as the first part of a forthcoming series of text mining applications in solar forecasting.

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348 citations

Cites background from "Latent dirichlet allocation"

...traced back to the works of Blei et al. (2003); Pritchard et al....
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...traced back to the works of Blei et al. (2003); Pritchard et al. (2000), which have a combined citation number of over 40,000....
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Book•

Recognizing Textual Entailment: Models and Applications

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Ido Dagan¹, Dan Roth², Mark Sammons², Fabio Massimo Zanzotto³•Institutions (3)

Bar-Ilan University¹, University of Illinois at Urbana–Champaign², University of Rome Tor Vergata³

01 Jul 2013

TL;DR: This book discusses the development of knowledge acquisition techniques in the field of text-based learning and discusses their applications in the context of education and research.

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Abstract: In the last few years, a number of NLP researchers have developed and participated in the task of Recognizing Textual Entailment (RTE). This task encapsulates Natural Language Understanding capabilities within a very simple interface: recognizing when the meaning of a text snippet is contained in the meaning of a second piece of text. This simple abstraction of an exceedingly complex problem has broad appeal partly because it can be conceived also as a component in other NLP applications, from Machine Translation to Semantic Search to Information Extraction. It also avoids commitment to any specific meaning representation and reasoning framework, broadening its appeal within the research community. This level of abstraction also facilitates evaluation, a crucial component of any technological advancement program. This book explains the RTE task formulation adopted by the NLP research community, and gives a clear overview of research in this area. It draws out commonalities in this research, detailing the intuitions behind dominant approaches and their theoretical underpinnings. This book has been written with a wide audience in mind, but is intended to inform all readers about the state of the art in this fascinating field, to give a clear understanding of the principles underlying RTE research to date, and to highlight the short- and long-term research goals that will advance this technology.

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348 citations

Journal Article•DOI•

Tensors for Data Mining and Data Fusion: Models, Applications, and Scalable Algorithms

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Evangelos E. Papalexakis¹, Christos Faloutsos², Nicholas D. Sidiropoulos³•Institutions (3)

University of California, Riverside¹, Carnegie Mellon University², University of Minnesota³

03 Oct 2016-ACM Transactions on Intelligent Systems and Technology

TL;DR: This survey presents some of the most widely used tensor decompositions, providing the key insights behind them, and summarizing them from a practitioner’s point of view.

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Abstract: Tensors and tensor decompositions are very powerful and versatile tools that can model a wide variety of heterogeneous, multiaspect data. As a result, tensor decompositions, which extract useful latent information out of multiaspect data tensors, have witnessed increasing popularity and adoption by the data mining community. In this survey, we present some of the most widely used tensor decompositions, providing the key insights behind them, and summarizing them from a practitioner’s point of view. We then provide an overview of a very broad spectrum of applications where tensors have been instrumental in achieving state-of-the-art performance, ranging from social network analysis to brain data analysis, and from web mining to healthcare. Subsequently, we present recent algorithmic advances in scaling tensor decompositions up to today’s big data, outlining the existing systems and summarizing the key ideas behind them. Finally, we conclude with a list of challenges and open problems that outline exciting future research directions.

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347 citations

Cites background or methods from "Latent dirichlet allocation"

.../ Parallel Bro and Andersson [1998] Kolda et al. [2005] Bader and Kolda [2007] Phan and Cichocki [2009] Kim and Candan [2012] Kang et al....
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...Finally, in a similar spirit to Huang et al. [2013], Anandkumar et al. [2014], and Huang et al. [2014] use the CP decomposition to compute the LDA [Blei et al. 2003] via the method of moments....
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...—Parallel/Distributed Computation: In conjunction with the rest of the techniques, many recent works have taken advantage of parallelization of the algorithms, or the use of existing high-end distributed computation environments (e.g., Hadoop/MapReduce), and thus achieve scalability....
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...Anandkumar et al. [2014] show that orthogonal CP can be used to estimate latent factor models such as Latent Dirichlet Allocation (LDA) and Mixed Membership Block ACM Transactions on Intelligent Systems and Technology, Vol. 8, No. 2, Article 16, Publication date: October 2016....
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...[2014] use the CP decomposition to compute the LDA [Blei et al. 2003] via the method of moments....
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References

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Open Access

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Book•

Handbook of mathematical functions : with formulas, graphs, and mathematical tables

[...]

Milton Abramowitz, Irene A. Stegun

01 Jan 1970

17,608 citations

Book•

Bayesian Data Analysis

[...]

Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin¹ - Show less +2 more•Institutions (1)

University of California, Irvine¹

01 Jan 1995

TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.

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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.

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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)....
[...]

Journal Article•DOI•

Indexing by Latent Semantic Analysis

[...]

Scott Deerwester¹, Susan T. Dumais², George W. Furnas², Thomas K. Landauer², Richard A. Harshman³ - Show less +1 more•Institutions (3)

University of Chicago¹, Telcordia Technologies², University of Western Ontario³

01 Sep 1990-Journal of the Association for Information Science and Technology

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.

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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.

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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)....
[...]

Book•

Introduction to Modern Information Retrieval

[...]

Gerard Salton, Michael J. McGill

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.

...read moreread less

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.

...read moreread less

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....
[...]

Book•

Theory of probability

[...]

Harold Jeffreys, R. Bruce Lindsay

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.

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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

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7,086 citations