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- W2136023766 abstract "This paper proposes a novel local discriminant component analysis (DCA) algorithm that is useful for pattern classification of high-dimensional data. Different from most traditional methods, in which feature extractors are usually used prior to a classifier, the proposed method incorporates the feature extraction process into the classifier. Then, a probabilistic neural network is developed based on the idea of local DCA, in which the whole network including the feature extractor and the classifier can be modulated according to a single training criterion, so that features suited to the classification purpose can be extracted. In this paper, a hybrid training algorithm is proposed on the basis of the minimum classification error (MCE) learning. In simulation experiments, benchmark data are used to prove feasibility of the proposed method" @default.
- W2136023766 created "2016-06-24" @default.
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- W2136023766 date "2005-10-24" @default.
- W2136023766 modified "2023-09-26" @default.
- W2136023766 title "Multivariate Pattern Classification based on Local Discriminant Component Analysis" @default.
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- W2136023766 doi "https://doi.org/10.1109/robio.2004.1521908" @default.
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