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- W2016459271 abstract "A reformative kernel algorithm, which can deal with two-class problems as well as those with more than two classes, on Fisher discriminant analysis is proposed. In the novel algorithm the supposition that in feature space discriminant vector can be approximated by some linear combination of a part of training samples, called significant nodes, is made. If the significant nodes are found out, the novel algorithm on kernel Fisher discriminant analysis will be superior to the naive one in classification efficiency. In this paper, a recursive algorithm for selecting significant nodes, is developed in detail. Experiments show that the novel algorithm is effective and much efficient in classifying." @default.
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- W2016459271 date "2004-10-01" @default.
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- W2016459271 title "An efficient renovation on kernel Fisher discriminant analysis and face recognition experiments" @default.
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- W2016459271 doi "https://doi.org/10.1016/j.patcog.2004.02.016" @default.
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