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- W1595499765 abstract "Kernal factor analysis (KFA) with varimax was proposed by using Mercer kernel function which can map the data in the original space to a high-dimensional feature space, and was compared with the kernel principle component analysis (KPCA). The results show that the best error rate in handwritten digit recognition by kernel factor analysis with varimax (4.2%) was superior to KPCA (4.4%). The KFA with varimax could more accurately image handwritten digit recognition." @default.
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- W1595499765 date "2006-10-01" @default.
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- W1595499765 title "Kernel Factor Analysis Algorithm with Varimax" @default.
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