Relation between PLSA and NMF and implications
Non-negative Matrix Factorization (NMF, ) and Probabilistic Latent Semantic Analysis (PLSA, ) 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 . 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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