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Kernel Discriminant Analysis Using Case-Specific Smoothing Parameters

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1 Author(s)
Anil K. Ghosh ; Theor. Stat. & Math. Unit, Indian Stat. Inst., Kotkata

In kernel discriminant analysis, one common practice is to use a fixed level of smoothing (estimated from training data) for classifying all unlabeled observations. But, in classification, a good choice of smoothing parameters also depends on the observation to be classified. Therefore, instead of using a fixed level of smoothing over the entire measurement space, it may be more useful to estimate the smoothing parameters depending on that specific observation. Here, we propose a simple method for this case-specific smoothing. Some benchmark data sets are analyzed to illustrate the performance of the proposed method.

Published in:

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)  (Volume:38 ,  Issue: 5 )