An Improved Kernel Minimum Square Error Classification Algorithm Based on L<inf>2,1</inf>-Norm Regularization

Publication Type:
Journal Article
Citation:
IEEE Access, 2017, 5 pp. 14133 - 14140
Issue Date:
2017-07-21
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© 2013 IEEE. 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 L2,1-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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