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Graph Regularized Nonnegative Matrix Factorization with Sparse Coding
In this paper, we propose a sparseness constraint NMF method, named graph regularized matrix factorization with sparse coding (GRNMF_SC). By combining manifold learning and sparse coding techniques together, GRNMF_SC can efficiently extract the basic vectors from the data space, which preserves the...
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| Huvudupphovsmän: | , |
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| Materialtyp: | Artigo |
| Språk: | Inglês |
| Publicerad: |
Hindawi Limited
2015-01-01
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| Serie: | Mathematical Problems in Engineering |
| Länkar: | http://dx.doi.org/10.1155/2015/239589 |
| Taggar: |
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