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Large-Scale Maximum Margin Discriminant Analysis Using Core Vector Machines
2008
IEEE Transactions on Neural Networks
Large-margin methods, such as support vector machines (SVMs), have been very successful in classification problems. Recently, maximum margin discriminant analysis (MMDA) was proposed that extends the large-margin idea to feature extraction. It often outperforms traditional methods such as kernel principal component analysis (KPCA) and kernel Fisher discriminant analysis (KFD). However, as in the SVM, its time complexity is cubic in the number of training points , and is thus computationally
doi:10.1109/tnn.2007.911746
pmid:18390308
fatcat:2ltpkp2sorhi3dvmvstldp7kcy