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20

ACML
2009
Springer

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Estimating Likelihoods for Topic Models

13 years 11 months ago

Estimating Likelihoods for Topic Models

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Abstract. Topic models are a discrete analogue to principle component analysis and independent component analysis that model topic at the word level within a document. They have many variants such as NMF, PLSI and LDA, and are used in many ﬁelds such as genetics, text and the web, image analysis and recommender systems. However, only recently have reasonable methods for estimating the likelihood of unseen documents, for instance to perform testing or model comparison, become available. This paper explores a number of recent methods, and improves their theory, performance, and testing.

Wray L. Buntine

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ACML 2009 | Component Analysis | Independent Component Analysis | Machine Learning | Topic Models |

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Added	25 May 2010
Updated	25 May 2010
Type	Conference
Year	2009
Where	ACML
Authors	Wray L. Buntine

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