Latent dirichlet allocation
Citations
169 citations
169 citations
169 citations
Cites methods from "Latent dirichlet allocation"
...Inspired by techniques from the text analysis literature, s everal recent papers analyze scenes using a spatially unstructured bag of features extracted from local image patches (see Fig. 1). In particular, latent Dirichlet allocation (LDA) [ 13 ] describes the features xji in image j using a K component mixture model with parametersµk....
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169 citations
Cites background from "Latent dirichlet allocation"
...Statistical topic models can not distinguish between these di.erent categories and produce topical descriptions that are mixtures of whos, whats, whens and wheres....
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169 citations
References
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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....
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...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)....
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...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)....
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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)....
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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....
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...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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7,086 citations