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Simultaneous sparse estimation of canonical vectors in the p>>N setting
Secondly, we propose an extension of this form to the $p\gg N$ setting and achieve feature selection by using a group penalty.
Supervised Classification Using Sparse Fisher's LDA
We apply a lasso-type penalty to the discriminant vector to ensure sparsity of the solution and use a shrinkage type estimator for the covariance matrix.
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