The Relationships Among Various Nonnegative Matrix Factorization Methods for Clustering

The nonnegative matrix factorization (NMF) has been shown recently to be useful for clustering. Various extensions of NMF have also been proposed. In this paper we present an overview and theoretically analyze the relationships among them. In addition, we clarify previously unaddressed issues, such as NMF normalization, cluster posterior probabilty, and NMF algoritm convergence rate. Experiments are also conducted to empirically evaluate and compare various factorization methods.

• Type: Conference
• Year: 2006
• Where: ICDM
• Authors: Tao Li, Chris H. Q. Ding
• Keywords: Data Mining, ICDM 2006, NMF Algoritm Convergence, NMF Normalization, Nonnegative Matrix Factorization