A Kernel-Induced Space Selection Approach to Model Selection in KLDA
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]
|Collections||ANU Research Publications|
|Source:||IEEE Transactions on Neural Networks|
|01_Wang_A_Kernel-Induced_Space_2008.pdf||1.48 MB||Adobe PDF||Request a copy|
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