Multichannel Extensions of Non-Negative Matrix Factorization With Complex-Valued Data

Non-negative matrix factorization

Non-negative Matrix Factorization with Sparseness Constraints

Non-negative matrix factorization (NMF) is a recently developed technique for finding parts-based, linear representations of non-negative data. Although it has successfully been applied in several applications, it does not always result in parts-based representations. In this paper, we show how explicitly incorporating the notion of ‘sparseness’ improves the found decompositions. Additionally, ...

Non-Negative Matrix Factorization with Sinkhorn Distance

Non-negative Matrix Factorization (NMF) has received considerable attentions in various areas for its psychological and physiological interpretation of naturally occurring data whose representation may be parts-based in the human brain. Despite its good practical performance, one shortcoming of original NMF is that it ignores intrinsic structure of data set. On one hand, samples might be on a m...

Non-Negative Matrix Factorization with Constraints

Non-negative matrix factorization (NMF), as a useful decomposition method for multivariate data, has been widely used in pattern recognition, information retrieval and computer vision. NMF is an effective algorithm to find the latent structure of the data and leads to a parts-based representation. However, NMF is essentially an unsupervised method and can not make use of label information. In t...

Classifying Faces with Non-negative Matrix Factorization

This paper addresses the well-known problem of recognizing faces under several unfavorable situations. We have analyzed situations with changes in expression, in illumination and occlusions such as faces wearing sunglasses or scarfs. We have introduced the use of the Non-negative Matrix Factorization (NMF) technique in the context of classification of face images and we have directly compared p...