An Improved Kernel Minimum Square Error Classification Algorithm Based on $L_{2,1}$ -Norm Regularization
TL;DR: An improved kernel minimum square error classification (IKMSEC) is proposed by using the <inline-formula> <tex-math notation="LaTeX">$L_{2,1}$ </tex- math></inline- formula>-norm regularization, which can obtain a sparse representation of nonlinear features to guarantee an efficient classification performance.
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Abstract: The kernel minimum square error classification (KMSEC) algorithm has been widely used in classification problems. It shows a good performance on image data besides the following drawbacks: not sparse in the solutions and sensitive to noises. The latter drawback will result in a decrease in the recognition performance. To this end, we propose an improved (IKMSEC) by using the $L_{2,1}$ -norm regularization, which can obtain a sparse representation of nonlinear features to guarantee an efficient classification performance. The comprehensive experiments show the promising results in face recognition and image classification.
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