Chapter 7
Nonnegative Matrix Factorizations for Clustering: A Survey
Tao Li
Florida International UniversityMiami, FLtaoli@cs.fiu.edu
Chris Ding
University of Texas at ArlingtonArlington, TXchqding@uta.edu
7.1 Introduction
Recently there has been significant development in the use of nonnegative matrix factorization (NMF) methods for various clustering tasks. NMF factorizes an input nonnegative matrix into two nonnegative matrices of lower rank. Although NMF can be used for conventional data analysis, the recent overwhelming interest in NMF is due to the newly discovered ability of NMF to solve challenging data mining and machine learning problems. In particular, NMF with the sum of squared error cost function is equivalent to a relaxed K-means ...
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