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ICDM
2008
IEEE

Nonnegative Matrix Factorization for Combinatorial Optimization: Spectral Clustering, Graph Matching, and Clique Finding

11 years 8 months ago
Nonnegative Matrix Factorization for Combinatorial Optimization: Spectral Clustering, Graph Matching, and Clique Finding
Nonnegative matrix factorization (NMF) is a versatile model for data clustering. In this paper, we propose several NMF inspired algorithms to solve different data mining problems. They include (1) multi-way normalized cut spectral clustering, (2) graph matching of both undirected and directed graphs, and (3) maximal clique finding on both graphs and bipartite graphs. Key features of these algorithms are (a) they are extremely simple to implement; and (b) they are provably convergent. We conduct experiments to demonstrate the effectiveness of these new algorithms. We also derive a new spectral bound for the size of maximal edge bicliques as a byproduct of our approach.
Chris H. Q. Ding, Tao Li, Michael I. Jordan
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICDM
Authors Chris H. Q. Ding, Tao Li, Michael I. Jordan
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