The Non-negative Matrix Factorization (NMF) is a popular technique for intelligent systems, which can be widely used to decompose nonnegative matrix into two factor matrices: basis and coefficient one, respectively. main objective of NMF ensure that the operation results matrices are as close original possible. Meanwhile, stability generalization ability algorithm should ensured. Therefore, per...

We propose a convolutive non-negative matrix factorization method to improve the intelligibility of speech signal in the context of adverse noise environment. The noise bases are prior learned with Non-negative Matrix Factorization (NMF) algorithm. A modified convolutive NMF with sparse constraint is then derived to extract speech bases from noisy speech. The divergence function is selected as ...

Nonnegative matrix factorization (NMF) is a popular dimension reduction technique that produces interpretable decomposition of the data into parts. However, this decompostion is not generally identifiable (even up to permutation and scaling). While other studies have provide criteria under which NMF is identifiable, we present the first (to our knowledge) characterization of the non-identifiabi...

In this paper, we propose a new ensemble document clustering method. The novelty of our method is the use of Non-negative Matrix Factorization (NMF) in the generation phase and a weighted hypergraph in the integration phase. In our experiment, we compared our method with some clustering methods. Our method achieved the best results.