A Review of Nonnegative Matrix Factorization Methods for Clustering

Ali Caner Türkmen

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arXiv · 1507.03194

A Review of Nonnegative Matrix Factorization Methods for Clustering

Ali Caner Türkmen

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Abstract

Nonnegative Matrix Factorization (NMF) was first introduced as a low-rank matrix approximation technique, and has enjoyed a wide area of applications. Although NMF does not seem related to the clustering problem at first, it was shown that they are closely linked. In this report, we provide a gentle introduction to clustering and NMF before reviewing the theoretical relationship between them. We then explore several NMF variants, namely Sparse NMF, Projective NMF, Nonnegative Spectral Clustering and Cluster-NMF, along with their clustering interpretations.

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stat.ML cs.LG cs.NA math.NA

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BibTeX RIS

Ali Caner Türkmen. 2015-07-12. A Review of Nonnegative Matrix Factorization Methods for Clustering. https://arxiv.org/abs/1507.03194

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