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•

Collaborative filtering for orkut communities: discovery of user latent behavior

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Wen-Yen Chen¹, Jon-Chyuan Chu², Junyi Luan³, Hongjie Bai⁴, Yi Wang⁴, Edward Y. Chang⁴ - Show less +2 more•Institutions (4)

University of California, Santa Barbara¹, Massachusetts Institute of Technology², Peking University³, Google⁴

20 Apr 2009

TL;DR: Empirical comparisons show that LDA performs consistently better than ARM for the community recommendation task when recommending a list of 4 or more communities, however, for recommendation lists of up to 3 communities, ARM is still a bit better.

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Abstract: Users of social networking services can connect with each other by forming communities for online interaction. Yet as the number of communities hosted by such websites grows over time, users have even greater need for effective community recommendations in order to meet more users. In this paper, we investigate two algorithms from very different domains and evaluate their effectiveness for personalized community recommendation. First is association rule mining (ARM), which discovers associations between sets of communities that are shared across many users. Second is latent Dirichlet allocation (LDA), which models user-community co-occurrences using latent aspects. In comparing LDA with ARM, we are interested in discovering whether modeling low-rank latent structure is more effective for recommendations than directly mining rules from the observed data. We experiment on an Orkut data set consisting of 492,104 users and 118,002 communities. Our empirical comparisons using the top-k recommendations metric show that LDA performs consistently better than ARM for the community recommendation task when recommending a list of 4 or more communities. However, for recommendation lists of up to 3 communities, ARM is still a bit better. We analyze examples of the latent information learned by LDA to explain this finding. To efficiently handle the large-scale data set, we parallelize LDA on distributed computers and demonstrate our parallel implementation's scalability with varying numbers of machines.

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174 citations

Journal Article•DOI•

A Multimodal Deep Neural Network for Human Breast Cancer Prognosis Prediction by Integrating Multi-Dimensional Data

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Dongdong Sun¹, Minghui Wang¹, Ao Li¹•Institutions (1)

University of Science and Technology of China¹

01 May 2019-IEEE/ACM Transactions on Computational Biology and Bioinformatics

TL;DR: This study proposes a Multimodal Deep Neural Network by integrating Multi-dimensional Data (MDNNMD) for the prognosis prediction of breast cancer and shows that the proposed method achieves a better performance than the prediction methods with single-dimensional data and other existing approaches.

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Abstract: Breast cancer is a highly aggressive type of cancer with very low median survival. Accurate prognosis prediction of breast cancer can spare a significant number of patients from receiving unnecessary adjuvant systemic treatment and its related expensive medical costs. Previous work relies mostly on selected gene expression data to create a predictive model. The emergence of deep learning methods and multi-dimensional data offers opportunities for more comprehensive analysis of the molecular characteristics of breast cancer and therefore can improve diagnosis, treatment, and prevention. In this study, we propose a Multimodal Deep Neural Network by integrating Multi-dimensional Data (MDNNMD) for the prognosis prediction of breast cancer. The novelty of the method lies in the design of our method's architecture and the fusion of multi-dimensional data. The comprehensive performance evaluation results show that the proposed method achieves a better performance than the prediction methods with single-dimensional data and other existing approaches. The source code implemented by TensorFlow 1.0 deep learning library can be downloaded from the Github: https://github.com/USTC-HIlab/MDNNMD.

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174 citations

Cites methods from "Latent dirichlet allocation"

...SVM and LDA methods [28] on discriminative tasks [29]....
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Proceedings Article•DOI•

Beyond LDA: Exploring Supervised Topic Modeling for Depression-Related Language in Twitter

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Philip Resnik¹, William Armstrong¹, Leonardo Claudino¹, Thang Nguyen¹, Viet-An Nguyen¹, Jordan Boyd-Graber¹ - Show less +2 more•Institutions (1)

University of Maryland, College Park¹

05 Jun 2015

TL;DR: This paper explores the use of supervised topic models in the analysis of linguistic signal for detecting depression, providing promising results using several models.

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Abstract: Topic models can yield insight into how depressed and non-depressed individuals use language differently. In this paper, we explore the use of supervised topic models in the analysis of linguistic signal for detecting depression, providing promising results using several models.

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174 citations

Journal Article•DOI•

Quantifying the semantics of search behavior before stock market moves

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Chester Curme¹, Chester Curme², Tobias Preis¹, H. Eugene Stanley², Helen Susannah Moat¹ - Show less +1 more•Institutions (2)

University of Warwick¹, Boston University²

12 Aug 2014-Proceedings of the National Academy of Sciences of the United States of America

TL;DR: An automated method that uses data from Google and Wikipedia to identify relevant topics in search data before large events successfully identifies historical links between searches related to business and politics and subsequent stock market moves.

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Abstract: Technology is becoming deeply interwoven into the fabric of society. The Internet has become a central source of information for many people when making day-to-day decisions. Here, we present a method to mine the vast data Internet users create when searching for information online, to identify topics of interest before stock market moves. In an analysis of historic data from 2004 until 2012, we draw on records from the search engine Google and online encyclopedia Wikipedia as well as judgments from the service Amazon Mechanical Turk. We find evidence of links between Internet searches relating to politics or business and subsequent stock market moves. In particular, we find that an increase in search volume for these topics tends to precede stock market falls. We suggest that extensions of these analyses could offer insight into large-scale information flow before a range of real-world events.

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174 citations

Cites methods from "Latent dirichlet allocation"

...To incorporate these insights, we analyze the semantic characteristics of all of the articles and words in the English version of Wikipedia using a modeling approach called latent Dirichlet allocation (LDA) (37)....
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...We configure the LDA to extract 100 different semantic topics from Wikipedia and provide lists of the 30 most representative words for each topic....
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...First, we take a large online corpus, Wikipedia, and use a well-known technique from computational linguistics (37) to identify lists of words constituting semantic topics within this corpus....
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...To extract semantic categories from the online encyclopedia Wikipedia, we build on a well-known observation (37) that words that frequently appear together in newspaper articles, encyclopedia entries, or other kinds of documents tend to bear semantic relationships to each other....
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When Training and Test Sets are Different: Characterising Learning Transfer

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Amos Storkey¹•Institutions (1)

University of Edinburgh¹

01 Jan 2013

TL;DR: There is potential for the development of models which capture the specific types of variations, combine different modes of variation, or do model selection to assess whether dataset shift is an issue in particular circumstances.

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Abstract: In this chapter, a number of common forms of dataset shift are introduced, and each is related to a particular form of causal probabilistic model. Examples are given for the different types of shift, and some corresponding modelling approaches. By characterising dataset shift in this way, there is potential for the development of models which capture the specific types of variations, combine different modes of variation, or do model selection to assess whether dataset shift is an issue in particular circumstances. As an example of how such models can be developed, an illustration is provided for one approach to adapting Gaussian process methods for a particular type of dataset shift called Mixture Component Shift.

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174 citations

Cites methods from "Latent dirichlet allocation"

...Latent Dirichlet Allocation [2] and many related techniques) are in fact methods for mixture component shift, applied to unsupervised problems in more general multidataset scenarios....
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...Many approaches to document analysis (e.g. Latent Dirichlet Allocation [2] and many related techniques) are in fact methods for mixture component shift, applied to unsupervised problems in more general multidataset scenarios....
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References

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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

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Bayesian Data Analysis

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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....
[...]
...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)....
[...]
...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.

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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....
[...]
...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