Optimizing data-dependent kernel using semi-supervised learning with pairwise constraints

被引：0
|
作者：
Wang, Na [1 ]
Liu, Guo-Sheng [1 ]
Li, Xia [1 ]
机构：
[1] College of Information Engineering, Shenzhen University, Shenzhen 518060, China
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2011年 / 24卷 / 05期
关键词：
Supervised learning - Principal component analysis;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The selection of kernel function and its parameters determine the performance of kernel function. A semi-supervised data-dependent kernel optimization algorithm is presented, which uses unlabeled data and pairwise constraints to maximize an objective function sensitive to data-dependent kernel, so that its performance is improved. Then the proposed method is employed to optimize the kernel of kernel principal components analysis (KPCA) and the experimental results of the classification and clustering performance on the artificial data and UCI data sets show its efficiency.
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页码：685 / 691
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