An efficient nonnegative matrix factorization approach in flexible kernel space

Authors:
Daoqiang Zhang;Wanquan Liu
Affiliations:
Dept. of CSE, Nanjing University of Aeronautics & Astronautics, China;Dept. of Computing, Curtin University of Technology, Australia
Venue:
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Year:
2009

Citing 7
Cited 1

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Abstract

In this paper, we propose a general formulation for kernel nonnegative matrix factorization with flexible kernels. Specifically, we propose the Gaussian nonnegative matrix factorization (GNMF) algorithm by using the Gaussian kernel in the framework. Different from a recently developed polynomial NMF (PNMF), GNMF finds basis vectors in the kernel-induced feature space and the computational cost is independent of input dimensions. Furthermore, we prove the convergence and nonnegativity of decomposition of our method. Extensive experiments compared with PNMF and other NMF algorithms on several face databases, validate the effectiveness of the proposed method.