SUMMARY Non-negative matrix factorization (NMF) is an increasingly used algorithm for the analysis of complex high-dimensional data. BRB-ArrayTools is a widely used software system for the analysis of gene expression data with almost 9000 registered users in over 65 countries. We have developed a NMF analysis plug-in in BRB-ArrayTools for unsupervised sample clustering of microarray gene expres...

We propose a new method to incorporate statistical priors on the solution of the nonnegative matrix factorization (NMF) for single-channel source separation (SCSS) applications. The Gaussian mixture model (GMM) is used as a log-normalized gain prior model for the NMF solution. The normalization makes the prior models energy independent. In NMF based SCSS, NMF is used to decompose the spectra of...

Non-negative matrix factorization (NMF) is a recently developed method for finding parts-based representation of non-negative data such as face images. Although it has successfully been applied in several applications, directly using NMF for face recognition often leads to low performance. Moreover, when performing on large databases, NMF needs considerable computational costs. In this paper, w...

In this paper, we use non-negative matrix factorization (NMF) to refine the document clustering results. NMF is a dimensional reduction method and effective for document clustering, because a term-document matrix is high-dimensional and sparse. The initial matrix of the NMF algorithm is regarded as a clustering result, therefore we can use NMF as a refinement method. First we perform min-max cu...

This letter presents theoretical, algorithmic, and experimental results about nonnegative matrix factorization (NMF) with the Itakura-Saito (IS) divergence. We describe how IS-NMF is underlaid by a well-defined statistical model of superimposed gaussian components and is equivalent to maximum likelihood estimation of variance parameters. This setting can accommodate regularization constraints o...

-Recently suggested non-negative matrix factorization (NMF) seems to overcome fundamental limitations of factor analysis at least in theoretical aspect. NMF cost function uses Poisson statistics as a noise model, rather than the Gaussian statistics, and provides a simple learning rule, in contrast to the tricky optimization in factor analysis. To study the feasibility of NMF for the analysis of...

The Non-negative Matrix Factorization (NMF) is a special low-rank approximation which allows for an additive parts-based and interpretable representation of the data. This article presents efforts to improve the convergence, approximation quality, and classification accuracy of NMF using five different meta-heuristics based on swarm intelligence. Several properties of the NMF objective function...

Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from hyperspectral image (HSI). Nonnegative matrix factorization (NMF) plays increasingly significant role to solve this problem. In article, we present comprehensive survey the NMF-based methods proposed for unmixing. Taking NMF model as baseline, show how improve by uti...

The maturational agent N-methylformamide (NMF) is an antitumor agent that also enhances the response of tumor cells in vitro to chemotherapeutic agents. Here, we tested whether NMF can improve therapy of the murine MCA-K mammary carcinoma with cis-diamminedichloroplatinum(II) (cis-DDP). Although the in vitro cell cultures of MCA-K tumor cells exhibited increased sensitivity to cis-DDP cytotoxic...

Nonnegative Matrix Factorization (NMF) approximates a given data matrix as a product of two low rank nonnegative matrices, usually by minimizing the L2 or the KL distance between the data matrix and the matrix product. This factorization was shown to be useful for several important computer vision applications. We propose here a new NMF algorithm that minimizes the Earth Mover’s Distance (EMD) ...