An efficient nonnegative matrix factorization approach in flexible Kernel space
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...
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Morgan Kaufmann Publishers Inc
2009
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| Online Access: | http://portal.acm.org/citation.cfm?id=1661661# http://hdl.handle.net/20.500.11937/31709 |
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curtin-20.500.11937-31709 |
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eprints |
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curtin-20.500.11937-317092017-10-02T02:27:47Z An efficient nonnegative matrix factorization approach in flexible Kernel space Zhang, D. Liu, Wan-quan Craig Boutilier 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. 2009 Conference Paper http://hdl.handle.net/20.500.11937/31709 http://portal.acm.org/citation.cfm?id=1661661# Morgan Kaufmann Publishers Inc fulltext |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
Curtin University Malaysia |
| building |
Curtin Institutional Repository |
| collection |
Online Access |
| description |
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. |
| author2 |
Craig Boutilier |
| author_facet |
Craig Boutilier Zhang, D. Liu, Wan-quan |
| format |
Conference Paper |
| author |
Zhang, D. Liu, Wan-quan |
| spellingShingle |
Zhang, D. Liu, Wan-quan An efficient nonnegative matrix factorization approach in flexible Kernel space |
| author_sort |
Zhang, D. |
| title |
An efficient nonnegative matrix factorization approach in flexible Kernel space |
| title_short |
An efficient nonnegative matrix factorization approach in flexible Kernel space |
| title_full |
An efficient nonnegative matrix factorization approach in flexible Kernel space |
| title_fullStr |
An efficient nonnegative matrix factorization approach in flexible Kernel space |
| title_full_unstemmed |
An efficient nonnegative matrix factorization approach in flexible Kernel space |
| title_sort |
efficient nonnegative matrix factorization approach in flexible kernel space |
| publisher |
Morgan Kaufmann Publishers Inc |
| publishDate |
2009 |
| url |
http://portal.acm.org/citation.cfm?id=1661661# http://hdl.handle.net/20.500.11937/31709 |
| first_indexed |
2018-09-06T21:47:45Z |
| last_indexed |
2018-09-06T21:47:45Z |
| _version_ |
1610896340095598592 |