Two-stage multiple kernel learning for supervised dimensionality reduction
作者：
Highlights：
• A novel approach for non-linear supervised dimensionality reduction is proposed.
• The proposed method uses kernel discriminant analysis method with new kernels.
• A multiple kernel learning (MKL) paradigm produces the new kernel function.
• Proper criteria for supervised dimensionality reduction are optimized in the MKL part.
• The proposed method is successfully evaluated using many real-world datasets.
摘要
•A novel approach for non-linear supervised dimensionality reduction is proposed.•The proposed method uses kernel discriminant analysis method with new kernels.•A multiple kernel learning (MKL) paradigm produces the new kernel function.•Proper criteria for supervised dimensionality reduction are optimized in the MKL part.•The proposed method is successfully evaluated using many real-world datasets.
论文关键词：Supervised dimensionality reduction,Multiple kernel learning,Objection recognition,Handwritten digit recognition,Pattern recognition
论文评审过程：Received 21 April 2014, Revised 5 November 2014, Accepted 2 December 2014, Available online 13 December 2014.
论文官网地址：https://doi.org/10.1016/j.patcog.2014.12.001
