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Nonlinear multiclass discriminant analysis
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DOI：10.1109/LSP.2003.813680.png)
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Abstract
En 中文
An alternative nonlinear multiclass discriminant algorithm is presented. This algorithm is based on the use of kernel functions and is designed to optimize a general linear discriminant analysis criterion based on scatter matrices. By reformulating these matrices in a specific form, a straightforward derivation allows the kernel function to be introduced in a simple and direct way. Moreover, we propose a method to determine the value of the regularization parameter tau, based on this derivation.
Keywords:
discriminant analysis
feature extraction
kernel method
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