Campus Units

Electrical and Computer Engineering, Industrial and Manufacturing Systems Engineering

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

6-15-2017

Journal or Book Title

IEEE Transactions on Signal Processing

Volume

65

Issue

12

First Page

3120

Last Page

3135

DOI

10.1109/TSP.2017.2679687

Abstract

Symmetric nonnegative matrix factorization (SymNMF) has important applications in data analytics problems such as document clustering, community detection, and image segmentation. In this paper, we propose a novel nonconvex variable splitting method for solving SymNMF. The proposed algorithm is guaranteed to converge to the set of Karush-Kuhn-Tucker (KKT) points of the nonconvex SymNMF problem. Furthermore, it achieves a global sublinear convergence rate. We also show that the algorithm can be efficiently implemented in parallel. Further, sufficient conditions are provided that guarantee the global and local optimality of the obtained solutions. Extensive numerical results performed on both synthetic and real datasets suggest that the proposed algorithm converges quickly to a local minimum solution.

Comments

This is a manuscript of an article published as Lu, Songtao, Mingyi Hong, and Zhengdao Wang. "A nonconvex splitting method for symmetric nonnegative matrix factorization: Convergence analysis and optimality." IEEE Transactions on Signal Processing (2017). DOI: 10.1109/TSP.2017.2679687. Posted with permission.

Rights

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Copyright Owner

IEEE

Language

en

File Format

application/pdf

Published Version

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