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- W2147740529 abstract "Abstract. We propose a general principle to extend dimension reduction tools to explicit dimension reduction mappings and we show that this can serve as an interface to incorporate prior knowledge in the form of class labels. We explicitly demonstrate this technique by combining locally linear mappings which result from matrix learning vector quantization schemes with the t-distributed stochastic neighbor embedding cost function. The technique is tested on several benchmark data sets." @default.
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- W2147740529 date "2011-01-01" @default.
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- W2147740529 title "Supervised dimension reduction mappings" @default.
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