Graph non-negative matrix factorization with alternative smoothed $$L_0$$ regularizations

Non-negative matrix factorization

Graph Regularized Non-negative Matrix Factorization By Maximizing Correntropy

Non-negative matrix factorization (NMF) has proved effective in many clustering and classification tasks. The classic ways to measure the errors between the original and the reconstructed matrix are l2 distance or KullbackLeibler (KL) divergence. However, nonlinear cases are not properly handled when we use these error measures. As a consequence, alternative measures based on nonlinear kernels,...

Topic Graph Based Non-negative Matrix Factorization for Transfer Learning

We propose a method called Topic Graph based NMF for Transfer Learning (TNT) based on Non-negative Matrix Factorization (NMF). Since NMF learns feature vectors to approximate the given data, the proposed approach tries to preserve the feature space which is spanned by the feature vectors to realize transfer learning. Based on the learned feature vectors in the source domain, a graph structure c...

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...