Abstract
With the advent of DNA microarrys, it is now possible to use the microarry data for tumor classification. Yet previous works have not use the nonnegative information of gene expression data for classification. In this paper, we propose a new method for tumor classification using gene expression data. In this method, we first extract new features of the gene expression data by virtue of non-negative matrix factorization (NMF) and its extension, i.e. sparse NMF (SNMF) then apply support vector machines (SVM) to classify the tumor samples using the extracted features. To better fit for classification aim, a new SNMF algorithm is also proposed.
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© 2008 Springer-Verlag Berlin Heidelberg
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Zhang, P., Zheng, CH., Li, B., Wen, CG. (2008). Tumor Classification Using Non-negative Matrix Factorization. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2008. Communications in Computer and Information Science, vol 15. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85930-7_32
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DOI: https://doi.org/10.1007/978-3-540-85930-7_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85929-1
Online ISBN: 978-3-540-85930-7
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