(Tao J W, Wang S T. Locality-preserved maximum information variance v-support vector machine[J]. Acta Automatic Sinica, 2012, 38(1): 97-108.)
[3]
Vapnik V N. The nature of statistical learning theory[M]. New York: Springer-Verlag, 1995: 59-87.
[4]
Bradford J R, West D R. Improved prediction of protein binding sites using a support vector machines approach[J]. Bioinformatics, 2005, 21(8): 1487-1494.
[5]
Tao Q, Chu D J, Wang J. Recursive support vector machines for dimensionality reduction[J]. IEEE Trans on Neural Networks, 2008, 19(1): 189-193.
[6]
Wu M R, Ye J P. A small sphere and large margin approach for novelty detection using training data with outliers[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2009, 31(11): 2088-2092.
[7]
Song H, Lee I, Zhao L, et al. Adaptive virtual support vector machine for reliability analysis of highdimensional problems[J]. Structural and Multidisciplinary Optimization, 2013, 47(4): 479-491.
[8]
Hwang J P, Choi B, Kim E. Multiclass lagrangian support vector machine[J]. Neural Computing and Applications, 2013, 22(3): 703-710.
[9]
Xue H, Chen S C, Yang Q. Structural support vector machine[J]. Lecture Notes in Computer Science, 2008, 5263(1): 501-511.
[10]
Shivaswamy P K, Jebara T. Maximum relative margin and data-dependent regularization[J]. J of Machine Learning Research, 2010, 32(11): 747-788.
[11]
Scholkopf B, Smola A J, Williamson R C, et al. New support vector algorithms[J]. Neural Computation, 2000, 12(5): 1207-1245.
[12]
Chapelle O, Vapnik V N, Bousquet O, et al. Choosing multiple parameters for support vector machines[J]. Machine Learning, 2002, 46(1): 131-159.
[13]
Suyken J A K, Brabanter J, De Lukas L, et al. Weighted least square support vector machines: Robustness and sparse approximation[J]. Neurocomputing, 2002, 48(4): 85-105.
[14]
Cao P, Zhao D Z, Zaiane O. An optimized cost-sensitive SVM for imbalanced data learning[J]. Lecture Notes in Computer Science, 2013, 7819(1): 280-292.
[15]
Peng X J, Xu D. Robust minimum class variance twin support vector machine classifier[J]. Neural Computing and Applications, 2013, 22(5): 999-1011.
[16]
Lanczos C. Linear differential operators[M]. London: Van Nostrand, 1964: 31-33.
[17]
Zafeiriou S, Tefas A, Pitas I. Minimum class variance support vector machines[J]. IEEE Trans on Image Processing, 2007, 16(10): 2551-2564.
[18]
Fisher R A. The use of multiple measurements in taxonomic problems[J]. Annals of Eugenics, 1936, 7(2): 179-188.
[19]
Cover T M, Hart P E. Nearest neighbor pattern classification[J]. IEEE Trans on Information Theory, 1967, 13(1): 21-27.
[20]
Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290(5500): 2323-2326.
[21]
Tenenbaum J B, Silva V, Langford J. A global geometric framework for nonlinear dimensionality reduction[J]. Science, 2000, 290(5500): 2319-2322.
[22]
Belkin M, Niyogi P. Laplacian eigenmaps and spectral techniques for embedding and clustering[J]. Advances in Neural Information Processing System, 2002, 14(9): 585-591.
[23]
Tao Y T, Yang J. The maximized discriminative subspace for manifold learning problem[J]. Lecture Notes in Computer Science, 2013, 7751(1): 784-792.
[24]
Belkin M, Niyogi P, Sindhwani V. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples[J]. J of Machine Learning Research, 2006, 7(12): 2399-2434.
[25]
Wang X M, Chung F L, Wang S T. On minimum class locality preserving variance support vector machine[J]. Pattern Recognition, 2010, 43(8): 2753-2762.
[26]
Gui J,Wang C, Zhu L. Locality preserving discriminant[J]. Lecture Notes in Computer Science, 2009, 5755(1): 566-572.
(Gao Q X, Xie D Y, Xu H, et al. Supervised feature extraction based on information fusion of local structure and diversity information[J]. Acta Automatic Sinica, 2010, 36(8): 1101-1114.)
(Xie J, Liu J. A new local discriminant projection method[J]. Chinese J of Computers, 2011, 34(11): 2243-2250.)
[31]
Wang F. A general learning framework using local and global regularization[J]. Pattern Recognition, 2010, 43(9): 3120-3129.
[32]
Yan S, Xu D, Zhang H, et al. Graph embedding and extensions: A general framework for dimensionality reduction[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2007, 29(1): 40-51.
[33]
WoodburyMA. Inverting modified matrices[R]. Princeton: Statistical Research Group, Institute for Advanced Study, 1950: 78-80.
[34]
Chun Y D. A generalization of the Sherman-Morrison-Woodbury formula[J]. Applied Mathematics Letters, 2011, 24(9): 1561-1564.
[35]
Simon H K. Neural networks and learning machines[M]. 3rd ed. Englewood Cliffs: Prentice Hall, 2009: 198-228.
[36]
Bartlett P L, Mendelson S. Rademacher and gaussian complexities: Risk bounds and structural results[J]. J of Machine Learning Research, 2002, 3(3): 463-482.
[37]
Koltchinskii V. Rademacher penalties and structural risk minimization[J]. IEEE Trans on Information Theory, 2001, 47(5): 1902-1914.
[38]
Sun S L. Multi-view Laplacian support vector machines[J]. Lecture Notes in Computer Science, 2011, 7121(1): 209-222.
[39]
Zhang T, Tao D, Li X, et al. Patch alignment for dimensionality reduction[J]. IEEE Trans on Knowledge and Data Engineering, 2009, 21(9): 1299-1313.
[40]
Saul L K, Roweis S T. Think globally, fit locally: Unsupervised learning of low dimensional manifolds[J]. J of Machine Learning Research, 2003, 4(12): 119-155.
[41]
Belhumeour P, Hespanha J, Kriegman D. Eigenfaces vs fisherfaces: Recognition using class specific linear projection[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720.
[42]
Xue H, Chen S C, Yang Q. Discriminatively regularized least-squares classification[J]. Pattern Recognition, 2009, 42(1): 93-104.
