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Latent Dirichlet Allocation for Text

Latent Dirichlet Allocation (LDA) is an unsupervised, statistical approach to document modeling that discovers latent semantic topics in large collections of text documents. LDA posits that words carry strong semantic information, and documents discussing similar topics will use a similar group of words. Latent topics are thus discovered by identifying groups of words in the corpus that frequently occur together within documents. In this way, LDA models documents as a random mixture over latent topics, with each topic being characterized by its own particular distribution over words. In this report, we show that LDA is not only useful in the text domain, but also in the image and music domain. In particular, we discuss algorithms that extend LDA to accomplish tasks like document classification for text, object localization for images, and automatic harmonic analysis for music. For each domain, we also emphasize approaches that go beyond LDA’s traditional bag-of-words representation to achieve more realistic models that incorporate order information.