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. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: Clustering; I.5.3 [Clustering]: Algorithms General Terms Algorithms Keywords Document clustering, probabilistic models, PLSA, NMF

• Type: Conference
• Year: 2005
• Where: SIGIR
• Authors: Éric Gaussier, Cyril Goutte
• Keywords: Document Clustering, Non-Negative Matrix Factorization, Probabilistic Latent Semantic Analysis, SIGIR 2005