Non-negative Matrix Factorization on Kernels

Abstract

In this paper, we extend the original non-negative matrix factorization (NMF) to kernel NMF (KNMF). The advantages of KNMF over NMF are: 1) it could extract more useful features hidden in the original data through some kernel-induced nonlinear mappings; 2) it can deal with data where only relationships (similarities or dissimilarities) between objects are known; 3) it can process data with negative values by using some specific kernel functions (e.g. Gaussian). Thus, KNMF is more general than NMF. To further improve the performance of KNMF, we also propose the SpKNMF, which performs KNMF on sub-patterns of the original data. The effectiveness of the proposed algorithms is validated by extensive experiments on UCI datasets and the FERET face database.