Latent dirichlet allocation
TLDR
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.read more
Citations
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Proceedings ArticleDOI
Toward harnessing user feedback for machine learning
Simone Stumpf,Vidya Rajaram,Lida Li,Margaret Burnett,Thomas G. Dietterich,Erin Sullivan,Russell Drummond,Jonathan L. Herlocker +7 more
TL;DR: The results show that user feedback has the potential to significantly improve machine learning systems, but that learning algorithms need to be extended in several ways to be able to assimilate this feedback.
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Cluster Canonical Correlation Analysis
TL;DR: A kernel extension, kernel cluster canonical correlation analysis (cluster-KCCA) is presented that extends clusterCCA to account for non-linear relationships and is shown to be computationally efficient, the complexity being similar to standard (K)CCA.
Proceedings ArticleDOI
Visualizing recommendations to support exploration, transparency and controllability
TL;DR: It is investigated how information visualization can improve user understanding of the typically black-box rationale behind recommendations in order to increase their perceived relevance and meaning and to support exploration and user involvement in the recommendation process.
Proceedings ArticleDOI
Mining geographic knowledge using location aware topic model
TL;DR: A Location Aware Topic Model (LATM) is proposed, a probabilistic graphical model, to explicitly model the relationships between locations and words.
Posted Content
Variational Inference in Nonconjugate Models
Chong Wang,David M. Blei +1 more
TL;DR: In this article, Laplace Variational Inference and delta method variational inference are proposed for nonconjugate models, which allow for easily derived variational algorithms with a wide class of nonconjoint models, and unify some of the existing algorithms that have been derived for specific models.
References
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Book
Bayesian Data Analysis
TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
Journal ArticleDOI
Indexing by Latent Semantic Analysis
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.
Book
Introduction to Modern Information Retrieval
Gerard Salton,Michael J. McGill +1 more
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.
Book
Theory of probability
Harold Jeffreys,R. Bruce Lindsay +1 more
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.