Collaborative and geometric multi-kernel learning for multi-class classification
作者：
Highlights：
• CGMKL realizes the multi-class classification under the MEKL framework through combining the softmax function and MEKL. By doing so, the MEKL enriches the expressions of sample and greatly improves the classification ability of the softmax function.
• CGMKL offers the complementary information between different kernel spaces by introducing a regularization term RU, which keeps consistency outputs of samples in different kernel spaces. By doing so, classifiers in different kernel spaces can learn from each other and keep collaborative working.
• CGMKL makes the output trend of data suit for classification through introducing a regularization term RG, which reduces the within-class distance of the outputs of samples. By doing so, the classification result exhibits a geometric feature.
摘要
•CGMKL realizes the multi-class classification under the MEKL framework through combining the softmax function and MEKL. By doing so, the MEKL enriches the expressions of sample and greatly improves the classification ability of the softmax function.•CGMKL offers the complementary information between different kernel spaces by introducing a regularization term RU, which keeps consistency outputs of samples in different kernel spaces. By doing so, classifiers in different kernel spaces can learn from each other and keep collaborative working.•CGMKL makes the output trend of data suit for classification through introducing a regularization term RG, which reduces the within-class distance of the outputs of samples. By doing so, the classification result exhibits a geometric feature.
论文关键词：Multi-class classification,Empirical kernel mapping,Multiple empirical kernel learning,Regularized learning
论文评审过程：Received 16 April 2019, Revised 31 July 2019, Accepted 12 September 2019, Available online 14 September 2019, Version of Record 28 October 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.107050
