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An Improved Kernel Minimum Square Error Classification Algorithm Based on L2,1-Norm Regularization
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DOI：10.1109/ACCESS.2017.2730218.png)
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Abstract
En 中文
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,L-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.
Keywords:
Minimum square error classification
kernel
L-2,L-1-norm
pattern recognition
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