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.

• Type: Journal
• Year: 2008
• Where: CORR
• Authors: Nikolaos Vasiloglou, Alexander G. Gray, David V. Anderson
• Keywords: CORR 2008, Education, Manifold Learning Technique, Non-Negative Matrix Factorization, Original Euclidean Distances