摘要: Subspace selection approaches are powerful tools in pattern classification and data visualization. One of the most important subspace approaches is the linear dimensionality reduction …
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Arnold Schönhage, Andreas F. W. Grotefeld, Ekkehart Vetter, Fast algorithms: A multitape Turing machine implementation ,(1994)
Trevor Hastie, Robert Tibshirani, Discriminant Analysis by Gaussian Mixtures Journal of the Royal Statistical Society: Series B (Methodological). ,vol. 58, pp. 155- 176 ,(1996) , 10.1111/J.2517-6161.1996.TB02073.X
Sandrine Dudoit, Jane Fridlyand, Terence P Speed, None, Comparison of discrimination methods for the classification of tumors using gene expression data Journal of the American Statistical Association. ,vol. 97, pp. 77- 87 ,(2002) , 10.1198/016214502753479248
Henry P. Decell, Shailesh M. Mayekar, Feature combinations and the divergence criterion Computers & Mathematics with Applications. ,vol. 3, pp. 71- 76 ,(1977) , 10.1016/0898-1221(77)90116-X
Hua Yu, Jie Yang, A direct LDA algorithm for high-dimensional data — with application to face recognition Pattern Recognition. ,vol. 34, pp. 2067- 2070 ,(2001) , 10.1016/S0031-3203(00)00162-X
Sarunas Raudys, Robert P.W. Duin, Expected classification error of the Fisher linear classifier with pseudo-inverse covariance matrix Pattern Recognition Letters. ,vol. 19, pp. 385- 392 ,(1998) , 10.1016/S0167-8655(98)00016-6
R. A. FISHER, THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS Annals of Human Genetics. ,vol. 7, pp. 179- 188 ,(1936) , 10.1111/J.1469-1809.1936.TB02137.X
Halima Bensmail, Gilles Celeux, Regularized Gaussian Discriminant Analysis through Eigenvalue Decomposition Journal of the American Statistical Association. ,vol. 91, pp. 1743- 1748 ,(1996) , 10.1080/01621459.1996.10476746
D.L. Swets, J.J. Weng, Using discriminant eigenfeatures for image retrieval IEEE Transactions on Pattern Analysis and Machine Intelligence. ,vol. 18, pp. 831- 836 ,(1996) , 10.1109/34.531802
M.A.T. Figueiredo, A.K. Jain, Unsupervised learning of finite mixture models IEEE Transactions on Pattern Analysis and Machine Intelligence. ,vol. 24, pp. 381- 396 ,(2002) , 10.1109/34.990138