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Gene Expression Data Classification Using Discriminatively Regularized Sparse Subspace Learning

Authors: Chunming Xu


Sparse representation which can represent high dimensional data effectively has been successfully used in computer vision and pattern recognition problems. However, it doesn-t consider the label information of data samples. To overcome this limitation, we develop a novel dimensionality reduction algorithm namely dscriminatively regularized sparse subspace learning(DR-SSL) in this paper. The proposed DR-SSL algorithm can not only make use of the sparse representation to model the data, but also can effective employ the label information to guide the procedure of dimensionality reduction. In addition,the presented algorithm can effectively deal with the out-of-sample problem.The experiments on gene-expression data sets show that the proposed algorithm is an effective tool for dimensionality reduction and gene-expression data classification.

Keywords: sparse representation, dimensionality reduction, labelinformation, sparse subspace learning, gene-expression data classification.

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[1] T.R. Golub,D.K. Slonim ,P. Tamayo ,et al, Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring, Science, vol.286,1999,pp. 531-537.
[2] I.T. Jolliffe,Principal Component Analysis.2nd edition. New York:Springer,2002.
[3] P. Comon, Independent Component Analysis-A New Concept?, Signal Process, vol.36,1994,pp.287-314.
[4] O.D. Richard,E.H. Peter and G.S. David, Pattern Classification,2nd edition. New York:Wiley-Interscience, 2000.
[5] S. Bicciato ,A. Luchini and C.D. Bello , PCA Disjoint Models for Multiclass Cancer Analysis using Gene Expression Data, Bioinformatics,vol.19,2003,pp.571-578.
[6] W. Liebermeister, Linear Modes of Gene Expression Determined by Independent Component Analysis, Bioinformatics, vol. 18,2002,pp. 51- 60.
[7] X.W. Zhang ,Y.L. Yap ,D. Wei ,et al . Molecular Diagnosis of Human Cancer Type by Gene Expression Profiles and Independent Component Analysis . European Journal of Human Genetics , vol.5,2005,pp.46-56.
[8] S. Dudoit, J. Fridlyand, and T. P. Speed, Comparison of Discrimination Methods for the Classification of Tumors using Gene Expression Data, Journal of the American Statistical Association, vol.97,2002,pp.77-87 .
[9] J.B.Tenenbaum,V.Silva and J.C.Langford,A global geometric framework for nonlinear dimensionality reduction,vol.290,2000,pp.2319-2323.
[10] S.T.Roweis,L.K.Saul,Nonlinear dimensionality reduction by locally linear embedding,vol.290,2000,pp.2323-2326.
[11] X. He and P. Niyogi, Locality Preserving Projections, Advances in Neural Information Processing Systems 16, , Cambridge,MIT Press, 2003.
[12] C. Shi and L.H. Chen, Feature Dimension Reduction for Microarray Data Analysis using Locally Linear Embedding. APBC,vol. 16, 2004,pp.1-7.
[13] G. Lee, C. Rodriguez and A. Madabhushi , Investigating the Efficacy of Nonlinear Dimensionality Reduction Schemes in Classifyi Gene- and Protein-Expression Studies, IEEE/ACM Transactions on Computational Biology and Bioinformatics,vol.5,2008, pp. -384.
[14] K. Huang and S. Aviyente, Sparse Representation for Signal Classification, Advances in Neural Information Processing Systems, vol.19,2006, pp. 609-616.
[15] S. Yan and H. Wang, Semi-Supervised Learning by Sparse Representation, SIAM International Conference on Data Mining, pp. 792-801 March,2009.
[16] John Wright, Yi Ma, Julien Mairal, et at, Sparse Representation For Computer Vision and Pattern Recognition .Proceedings of International Conference on Computer Vision and Pattern Recognition, vol.98,2010, pp. 1031-1044.
[17] H. Xue, S.C. Chen, Q. Yang, Discriminatively Regularized Least- Squares Classification. Pattern Recognition,vol.42,2009,pp. 93-104.
[18] S. Pomeroy , P. Tamayo and M. Gaasenbeek, et al, Prediction of central nervous system embryonal tumour outcome based on gene expression, Nature,vol.415,2002,pp.436-442.
[19] T. R. Golub, D. K. Slonim and P. T., et al. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring, 1999,Science,vol.286, pp.531-537 .
[20] U. Alon and N. Bkraai and D.A. Notterman, et al,Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays, Proceedings of the National Academy of Sciences,vol.96,1999 ,pp.6745-6750.
[21] S. Deegalla and H. Bostrom,Classification of microarrays with kNN: comparison of dimensionality reduction methods, Lecture Notes in Computer Science,vol.4881,2007,pp.800-809.
[22] P. Helman ,R. Veroff and S.R. Atlas , et al,A Bayesian network classification methodology for gene expression data, Journal of Computational Biology,vol.11,2004,pp.581-615.
[23] T.S.Furey,N.Cristianini and N.Duffy,et al, Support vector machines classification and validation of cancer tissue samples using microarray expression data,Bioinformatics,vol.16,2000, pp.906-914.