Deep multiple multilayer kernel learning in core vector machines
作者：
Highlights：
• Developed a scalable multilayer multi-kernel learning in kernel machines.
• The proposed core vector machine framework uses multiple layers of fea- ture extraction.
• The feature extraction method uses Kernel PCA with Multiple Kernel Learning.
• The unsupervised multiple kernel learning method employs single/multilayer kernels.
• Empirical results clearly show the better performance of the proposed method.
摘要
•Developed a scalable multilayer multi-kernel learning in kernel machines.•The proposed core vector machine framework uses multiple layers of fea- ture extraction.•The feature extraction method uses Kernel PCA with Multiple Kernel Learning.•The unsupervised multiple kernel learning method employs single/multilayer kernels.•Empirical results clearly show the better performance of the proposed method.
论文关键词：Deep kernel machines,Unsupervised MKL,Core vector machines,Scalability,Arc-cosine kernel,Multilayer kernel learning
论文评审过程：Received 15 June 2017, Revised 22 November 2017, Accepted 30 November 2017, Available online 2 December 2017, Version of Record 22 December 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.11.058
