TY - GEN
T1 - Estimating likelihoods for topic models
AU - Buntine, Wray
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=70549102400&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-05224-8_6
DO - 10.1007/978-3-642-05224-8_6
M3 - Conference contribution
SN - 3642052231
SN - 9783642052231
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 51
EP - 64
BT - Advances in Machine Learning - First Asian Conference on Machine Learning, ACML 2009, Proceedings
T2 - 1st Asian Conference on Machine Learning, ACML 2009
Y2 - 2 November 2009 through 4 November 2009
ER -