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CORR
2008
Springer

Non-Negative Matrix Factorization, Convexity and Isometry

8 years 2 months ago
Non-Negative Matrix Factorization, Convexity and Isometry
Traditional Non-Negative Matrix Factorization (NMF) [19] is a successful algorithm for decomposing datasets into basis function that have reasonable interpretation. One problem of NMF is that the original Euclidean distances are not preserved. Isometric embedding (IE) is a manifold learning technique that traces the intrinsic dimensionality of a data set, while preserving local distances. In this paper we propose a hybrid method of NMF and IE IsoNMF. IsoNMf combines the advantages of both NMF and Isometric Embedding and it gives a much more compact spectrum compared to the original NMF.
Nikolaos Vasiloglou, Alexander G. Gray, David V. A
Added 09 Dec 2010
Updated 09 Dec 2010
Type Journal
Year 2008
Where CORR
Authors Nikolaos Vasiloglou, Alexander G. Gray, David V. Anderson
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