Nonnegative matrix factorization with quadratic programming
Neurocomputing
Blind Image Separation Using Nonnegative Matrix Factorization with Gibbs Smoothing
Neural Information Processing
Computational Intelligence and Neuroscience - Advances in Nonnegative Matrix and Tensor Factorization
Clustering gene expression data for periodic genes based on INMF
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
Non-monotone projection gradient method for non-negative matrix factorization
Computational Optimization and Applications
Solving non-negative matrix factorization by alternating least squares with a modified strategy
Data Mining and Knowledge Discovery
Modified subspace Barzilai-Borwein gradient method for non-negative matrix factorization
Computational Optimization and Applications
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In this paper, we propose a novel approach, which is called as nonnegative matrix factorization (NMF), to analyze genome wide expression data. One of NMF advantages is that it can directly process these data without normalization. Firstly, we design an optimal algorithm for NMF approach. Compared with the existing NMF algorithms, our algorithm is more stable and converges very fast. We have coded the final algorithm in highly optimized C. Secondly; we describe the use of NMF in the extraction of the characteristic patterns from genome wide expression data. Thirdly, some simulation experiments are made in order to verify the efficiency of NMF algorithm. our conclusions are that NMF can be used as a powerful tool to extract the biologically meaningful expression patterns from genomic wide expression data.