
This Article  
 
Share  
Bibliographic References  
Add to:  
Digg Furl Spurl Blink Simpy Del.icio.us Y!MyWeb  
Search  
 
ASCII Text  x  
Qiang Cheng, "A Sparse Learning Machine for HighDimensional Data with Application to Microarray Gene Analysis," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 7, no. 4, pp. 636646, OctoberDecember, 2010.  
BibTex  x  
@article{ 10.1109/TCBB.2009.8, author = {Qiang Cheng}, title = {A Sparse Learning Machine for HighDimensional Data with Application to Microarray Gene Analysis}, journal ={IEEE/ACM Transactions on Computational Biology and Bioinformatics}, volume = {7}, number = {4}, issn = {15455963}, year = {2010}, pages = {636646}, doi = {http://doi.ieeecomputersociety.org/10.1109/TCBB.2009.8}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE/ACM Transactions on Computational Biology and Bioinformatics TI  A Sparse Learning Machine for HighDimensional Data with Application to Microarray Gene Analysis IS  4 SN  15455963 SP636 EP646 EPD  636646 A1  Qiang Cheng, PY  2010 KW  Highdimensional data KW  feature selection KW  persistence KW  bias KW  convex optimization KW  primaldual interiorpoint optimization KW  cancer classification KW  microarray gene analysis. VL  7 JA  IEEE/ACM Transactions on Computational Biology and Bioinformatics ER   
[1] D. Ghosh, "Singular Value Decomposition Regression Modeling for Classification of Tumors from Microarray Experiments," Proc. Pacific Symp. Biocomputing, pp. 1146211467, 2002.
[2] J. Fan and Y. Fan, "High Dimensional Classification Using Features Annealed Independence Rules," Annals of Statistics, vol. 36, pp. 26052637, 2008.
[3] I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, "Gene Selection for Cancer Classification Using Support Vector Machines," Machine Learning, vol. 46, pp. 389422, 2002.
[4] V.N. Vapnik, Statistical Learning Theory. Wiley Interscience 1998.
[5] V.N. Vapnik, The Nature of Statistical Learning Theory. Springer, 1999.
[6] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning. Springer, 2001.
[7] T. Furey, N. Cristianini, N. Duffy, D. Bednarski, M. Schummer, and D. Haussler, "Support Vector Machine Classification and Validation of Cancer Tissue Samples Using Microarray Expression Data," Bioinformatics, vol. 16, pp. 906914, 2000.
[8] I.T. Jolliffe, Principal Component Analysis. SpringerVerlag, 1986.
[9] R. Tibshirani, T. Hastie, B. Narasimhan, and G. Chu, "Diagnosis of Multiple Cancer Types by Shrunken Centroids of Gene Expression," Proc. Nat'l Academy of Sciences USA, vol. 99, pp. 65676572, 2002.
[10] P. Bickel and E. Levina, "Some Theory of Fisher's Linear Discriminant Function, 'Naive Bayes', and Some Alternatives Where There are Many More Variables Than Observations," Bernoulli, vol. 10, pp. 9891010, 2004.
[11] M. Wu, B. Scholkopf, and G. Bakir, "A Direct Method for Building Sparse Kernel Learning Algorithms," J. Machine Learning Research, vol. 7, pp. 603624, 2006.
[12] J. Zhu, S. Rosset, T. Hastie, and R. Tibshirani, "1Norm Support Vector Machines," Proc. Neural Information Processing Systems, 2003.
[13] J. Fan and R. Li, "Statistical Challenges with High Dimensionality: Feature Selection in Knowledge Discovery," Proc. Int'l Congress of Mathematicians, 2006.
[14] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge Univ. Press, 2004.
[15] H. Liu and H. Motoda, Feature Selection for Knowledge Discovery and Data Mining. Springer, 1998.
[16] P. Geladi and B. Kowalski, "Partial LeastSquares Regression: A Tutorial," Analytica Chemica Acta, vol. 185, pp. 117, 1986.
[17] M. Barker and W. Rayens, "Partial Least Squares for Discrimination," J. Chemometrics, vol. 17, no. 3, pp. 166173, 2003.
[18] K.C. Li, "Sliced Inverse Regression for Dimension Reduction (with Discussion)," J. Am. Statistical Assoc., vol. 86, pp. 316342, 1991.
[19] E. Bair, T. Hastie, P. Debashis, and R. Tibshirani, "Prediction by Supervised Principal Components," J. Am. Statistical Assoc., vol. 101, pp. 119137, 2006.
[20] D. Nguyen and D. Rocke, "Tumor Classification by Partial Least Squares Using Microarray Gene Expression Data," Bioinformatics, vol. 18, pp. 3950, 2002.
[21] X. Huang and W. Pan, "Linear Regression and TwoClass Classification with Gene Expression Data," Bioinformatics, vol. 19, pp. 20722078, 2003.
[22] F. Chiaromonte and J. Martinelli, "Dimension Reduction Strategies for Analyzing Global Gene Expression Data with a Response," Math. Biosciences, vol. 176, pp. 123144, 2002.
[23] A. Antoniadis, S. LambertLacroix, and F. Leblanc, "Effective Dimension Reduction Methods for Tumor Classification Using Gene Expression Data," Bioinformatics, vol. 19, pp. 563570, 2003.
[24] E. Bura and R. Pfeiffer, "Graphical Methods for Class Prediction Using Dimension Reduction Techniques on DNA Microarray Data," Bioinformatics, vol. 19, pp. 12521258, 2003.
[25] A. Martinez and A. Kak, "PCA versus LDA," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 228233, Feb. 2001.
[26] T. Golub et al., "Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring," Science, vol. 286, pp. 531537, http://www.broad.mit.edu/ cgibin/ cancerdata sets.cgi, 1999.
[27] L. Shen and E. Tan, "PLS and SVD Based Penalized Logistic Regression for Cancer Classification Using Microarray Data," Proc. Third AsiaPacific Bioinformatics Conf., P. Chen and L. Wong, eds., pp. 219228, Jan. 2005.
[28] S. vg, D. Slocaj, and A. Leonardis, "Combining Reconstructive and Discriminative Subspace Methods for Robust Classification and Regression by Subsampling," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 3, pp. 337350, Mar. 2006.
[29] E. Greenshtein, "Best Subset Selection, Persistence in HighDimensional Statistical Learning and Optimization under $l_1$ Constraint," Annals of Statistics, vol. 34, no. 5, pp. 23672386, 2006.
[30] D. Foster and E. George, "The Risk Inflation Criterion for Multiple Regression," Annals of Statistics, vol. 22, pp. 19471975, 1994.
[31] D. Donoho and X. Huo, "Uncertainty Principles and Ideal Atomic Decomposition," IEEE Trans. Information Theory, vol. 47, no. 7, pp. 28452862, Nov. 2001.
[32] M. Elad and A.M. Bruckstein, "A Generalized Uncertainty Principle and Sparse Representation in Pairs of Bases," IEEE Trans. Information Theory, vol. 48, no. 9, pp. 25582567, Sept. 2002.
[33] D. Donoho, "For Most Large Underdetermined Systems of Linear Equations the Minimal $l_1$ Norm Solution is Also the Sparsest Solution," Comm. Pure and Applied Math., vol. 59, no. 6, pp. 797829, 2006.
[34] P. Rousseeuw and A. Leroy, Robust Regression and Outlier Detection. Wiley, 1987.
[35] S. Dudoit, J. Fridlyand, and T. Speed, "Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data," J. Am. Statistical Assoc., vol. 97, pp. 7787, 2002.
[36] D. Singh et al., "Gene Expression Correlates of Clinical Prostate Cancer Behavior," Cancer Cell, vol. 1, pp. 203209, http://www.broad.mit.edu/cgibin/cancerdata sets.cgi , 2002.
[37] J. Welsh et al., "Analysis of Gene Expression Identifies Candidate Markers and Pharmacological Targets in Prostate Cancer," Cancer Research, vol. 61, pp. 59745978, 2001.
[38] http:/www.chestsurg.org, 2009.