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A Kernel-Induced Space Selection Approach to Model Selection in KLDA

Wang, Lei; Chan, Kap Luk; Xue, Ping; Zhou, Luping


Model selection in kernel linear discriminant analysis (KLDA) refers to the selection of appropriate parameters of a kernel function and the regularizer. By following the principle of maximum information preservation, this paper formulates the model selection problem as a problem of selecting an optimal kernel-induced space in which different classes are maximally separated from each other. A scatter-matrix-based criterion is developed to measure the "goodness" of a kernel-induced space, and...[Show more]

CollectionsANU Research Publications
Date published: 2008
Type: Journal article
Source: IEEE Transactions on Neural Networks
DOI: 10.1109/TNN.2008.2005140


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