PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Relation between PLSA and NMF and Implications
Cyril Goutte and Eric Gaussier
In: SIGIR 2005, 15-19 August 2005, Salvador, Brazil.

Abstract

The techniques of Non-negative Matrix Factorisation (NMF, [5]) and Probabilistic Latent Semantic Analysis (PLSA, [4]) have been succesfully applied to a number of text analysis tasks such as document clustering. Despite their different inspirations, these methods are both instances of multinomial PCA [1]. We further explore this relationship and first show that PLSA solves the problem of NMF with KL divergence, and then explore the implications of this relationship.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Natural Language Processing
Information Retrieval & Textual Information Access
ID Code:971
Deposited By:Cyril Goutte
Deposited On:19 May 2005