An Improved Kernel Minimum Square Error Classification Algorithm Based on $L_{2,1}$ -Norm Regularization

An Improved Kernel Minimum Square Error Classification Algorithm Based on $L_{2,1}$ -Norm Regularization

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 recog...

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Bibliographic Details
Main Authors: Zhonghua Liu, Shan Xue, Lin Zhang, Jiexin Pu, Haijun Wang
Format: Article
Language:English
Published: IEEE 2017-01-01
Series:IEEE Access
Subjects:
Minimum square error classification
kernel
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pattern recognition
Online Access:https://ieeexplore.ieee.org/document/7987683/
Description
Summary: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<sub>2,1</sub>-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
ISSN:2169-3536

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