Relation between PLSA and NMF and implications


Non-negative Matrix Factorization (NMF, [5]) and Probabilistic Latent Semantic Analysis (PLSA, [4]) have been successfully applied to a number of text analysis tasks such as document clustering. Despite their different inspirations, both methods are 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.

DOI: 10.1145/1076034.1076148

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@inproceedings{Gaussier2005RelationBP, title={Relation between PLSA and NMF and implications}, author={{\'E}ric Gaussier and Cyril Goutte}, booktitle={SIGIR}, year={2005} }