Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Document clustering using nonnegative matrix factorization
Information Processing and Management: an International Journal
Improving Tumor Clustering Based on Gene Selection
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Tumor clustering using nonnegative matrix factorization with gene selection
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
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In cancer diagnosis and treatment, clustering based on gene expression data has been shown to be a powerful method in cancer class discovery. In this paper, we discuss the use of nonnegative matrix factorization with sparseness constraints (NMFSC), a method which can be used to learn a parts representation of the data, to analysis gene expression data. We illustrate how to choose appropriate sparseness factors in the algorithm and demonstrate the improvement of NMFSC by direct comparison with the nonnegative matrix factorization (NMF). In addition, when using it on the two well-studied datasets, we obtain pretty much the same results with the sparse non-negative matrix factorization (SNMF).