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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Journal ArticleDOI
Unsupervised Visual Hashing with Semantic Assistant for Content-Based Image Retrieval
TL;DR: This study proposes a novel unsupervised visual hashing approach called semantic-assisted visual hashing (SAVH), distinguished from semi-supervised and supervised visual hashing, to effectively extract the rich semantics latently embedded in auxiliary texts of images to boost the effectiveness of visual hashing without any explicit semantic labels.
Proceedings ArticleDOI
Unsupervised Topic Modelling for Multi-Party Spoken Discourse
TL;DR: It is shown how Bayesian inference in this generative model can be used to simultaneously address the problems of topic segmentation and topic identification: automatically segmenting multi-party meetings into topically coherent segments with performance which compares well with previous unsupervised segmentation-only methods.
Journal ArticleDOI
Frequentist Consistency of Variational Bayes
Yixin Wang,David M. Blei +1 more
TL;DR: It is proved that the VB posterior converges to the Kullback–Leibler (KL) minimizer of a normal distribution, centered at the truth and the corresponding variational expectation of the parameter is consistent and asymptotically normal.
Journal ArticleDOI
Bag-of-Words Representation in Image Annotation: A Review
TL;DR: This paper reviews related works based on the issues of improving and/or applying BoW for image annotation to automatically assign keywords to images, so image retrieval users are able to query images by keywords.
Book ChapterDOI
Variational Extensions to EM and Multinomial PCA
TL;DR: In this paper, a review of the basic theory of the variational extension to the expectation-maximization algorithm is presented, and then discrete component finding algorithms are presented in that light.
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.
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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.