Latent dirichlet allocation
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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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An Overview of Cross-media Retrieval: Concepts, Methodologies, Benchmarks and Challenges
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Journal ArticleDOI
What is wrong with topic modeling? And how to fix it using search-based software engineering
TL;DR: LDADE, a search-based software engineering tool which uses Differential Evolution (DE) to tune the LDA’s parameters, is used to provide a method in which distributions generated by LDA are more stable and can be used for further analysis.
Proceedings ArticleDOI
Short text classification improved by learning multi-granularity topics
TL;DR: This paper proposes an method to leverage topics at multiple granularity, which can model the short text more precisely and compared the proposed method with the state-of-the-art baseline over one open data set.
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Describing Visual Scenes Using Transformed Objects and Parts
TL;DR: This work develops hierarchical, probabilistic models for objects, the parts composing them, and the visual scenes surrounding them and proposes nonparametric models which use Dirichlet processes to automatically learn the number of parts underlying each object category, and objects composing each scene.
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