On the clustering aspect of nonnegative matrix factorization
Machine Learning
2010-06-15 v2
Abstract
This paper provides a theoretical explanation on the clustering aspect of nonnegative matrix factorization (NMF). We prove that even without imposing orthogonality nor sparsity constraint on the basis and/or coefficient matrix, NMF still can give clustering results, thus providing a theoretical support for many works, e.g., Xu et al. [1] and Kim et al. [2], that show the superiority of the standard NMF as a clustering method.
Cite
@article{arxiv.1005.5462,
title = {On the clustering aspect of nonnegative matrix factorization},
author = {Andri Mirzal and Masashi Furukawa},
journal= {arXiv preprint arXiv:1005.5462},
year = {2010}
}
Comments
4 pages, no figure, to appear in ICEIE 2010