A Sparse Learning Machine for High-Dimensional Data with Application to Microarray Gene Analysis
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[1] Nello Cristianini,et al. Support vector machine classification and validation of cancer tissue samples using microarray expression data , 2000, Bioinform..
[2] Peter J. Rousseeuw,et al. Robust regression and outlier detection , 1987 .
[3] S. Dudoit,et al. Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data , 2002 .
[4] R. Tibshirani,et al. Diagnosis of multiple cancer types by shrunken centroids of gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.
[5] Wei Pan,et al. Linear regression and two-class classification with gene expression data , 2003, Bioinform..
[6] J. Mesirov,et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.
[7] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.
[8] Sanja Fidler,et al. Combining reconstructive and discriminative subspace methods for robust classification and regression by subsampling , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[9] Jianqing Fan,et al. High Dimensional Classification Using Features Annealed Independence Rules. , 2007, Annals of statistics.
[10] Ker-Chau Li. Sliced inverse regression for dimension reduction (with discussion) , 1991 .
[11] Xiaoming Huo,et al. Uncertainty principles and ideal atomic decomposition , 2001, IEEE Trans. Inf. Theory.
[12] Runze Li,et al. Statistical Challenges with High Dimensionality: Feature Selection in Knowledge Discovery , 2006, math/0602133.
[13] Heng Tao Shen,et al. Principal Component Analysis , 2009, Encyclopedia of Biometrics.
[14] Avinash C. Kak,et al. PCA versus LDA , 2001, IEEE Trans. Pattern Anal. Mach. Intell..
[15] B. Kowalski,et al. Partial least-squares regression: a tutorial , 1986 .
[16] Ruth M. Pfeiffer,et al. Graphical Methods for Class Prediction Using Dimension Reduction Techniques on DNA Microarray Data , 2003, Bioinform..
[17] E. Greenshtein. Best subset selection, persistence in high-dimensional statistical learning and optimization under l1 constraint , 2006, math/0702684.
[18] Eric R. Ziegel,et al. The Elements of Statistical Learning , 2003, Technometrics.
[19] P. Bickel,et al. Some theory for Fisher''s linear discriminant function , 2004 .
[20] Debashis Ghosh,et al. Singular Value Decomposition Regression Models for Classification of Tumors from Microarray Experiments , 2001, Pacific Symposium on Biocomputing.
[21] J. Welsh,et al. Analysis of gene expression identifies candidate markers and pharmacological targets in prostate cancer. , 2001, Cancer research.
[22] Dean P. Foster,et al. The risk inflation criterion for multiple regression , 1994 .
[23] Danh V. Nguyen,et al. Tumor classification by partial least squares using microarray gene expression data , 2002, Bioinform..
[24] R. Tibshirani,et al. Prediction by Supervised Principal Components , 2006 .
[25] Bernhard Schölkopf,et al. A Direct Method for Building Sparse Kernel Learning Algorithms , 2006, J. Mach. Learn. Res..
[26] Ron Kohavi,et al. Feature Selection for Knowledge Discovery and Data Mining , 1998 .
[27] Peter J. Rousseeuw,et al. Robust Regression and Outlier Detection , 2005, Wiley Series in Probability and Statistics.
[28] Michael Elad,et al. A generalized uncertainty principle and sparse representation in pairs of bases , 2002, IEEE Trans. Inf. Theory.
[29] M. Barker,et al. Partial least squares for discrimination , 2003 .
[30] E. Lander,et al. Gene expression correlates of clinical prostate cancer behavior. , 2002, Cancer cell.
[31] D. Donoho. For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution , 2006 .
[32] Li Shen,et al. PLS and SVD based penalized logistic regression for cancer classification using microarray data , 2005, APBC.
[33] Sophie Lambert-Lacroix,et al. Effective dimension reduction methods for tumor classification using gene expression data , 2003, Bioinform..
[34] Robert Tibshirani,et al. 1-norm Support Vector Machines , 2003, NIPS.
[35] Ker-Chau Li,et al. Sliced Inverse Regression for Dimension Reduction , 1991 .
[36] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[37] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[38] F. Chiaromonte,et al. Dimension reduction strategies for analyzing global gene expression data with a response. , 2002, Mathematical biosciences.