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Collaborative representation based classifier with maximum correntropy criterion and locality constraint
- Chinese Journal of Intelligent Science and Technology Vol. 3, Issue 3, Pages: 334-341(2021)
- 作者机构：
- 作者简介：
- 基金信息：
DOI：10.11959/j.issn.2096-6652.202134
CLC： TP391.4-
Published Online：2021-09，
Published：15 September 2021
- 稿件说明：
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Qinru YU, Guifu LU. Collaborative representation based classifier with maximum correntropy criterion and locality constraint[J]. Chinese journal of intelligent science and technology, 2021, 3(3): 334-341.
Qinru YU, Guifu LU. Collaborative representation based classifier with maximum correntropy criterion and locality constraint[J]. Chinese journal of intelligent science and technology, 2021, 3(3): 334-341. DOI： 10.11959/j.issn.2096-6652.202134.
摘要
提出一种基于最大相关熵和局部约束的协同表示分类器（CRC/MCCLC），该分类器能同时利用最大相关熵和局部信息。一方面，通过利用最大相关熵准则，CRC/MCCLC不仅在异常值处理上比L
1
范数
鲁棒性更高，还可以使用半二次优化技术进行更有效的计算；另一方面，CRC/MCCLC 通过使用局部信息得到近似稀疏表示，以此从训练样本中获得更多的判别信息。在 ORL、Yale 以及 AR 人脸数据集等图像数据集上的实验结果验证了CRC/MCCLC 方法的有效性。
Abstract
A method which utilizes maximum correntropy criterion and locality information called collaborative representation based classifier with maximum correntropy criterion and locality constraint (CRC/MCCLC) was proposed.On the one hand
CRC/MCCLC was not only more robust to outliers than L
1
norm but also could be computed efficiently using half-quadratic optimization technique because of the use of maximum correntropy criterion.On the other hand
CRC/MCCLC could obtain more discriminative information from the training samples and could lead to an approximately sparse representation because of the use of locality information.Extensive experimental results on some image databases demonstrate that CRC/MCCLC can achieve the state-of-the-art performance on these image databases.
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references
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