| 基于核的慢特征分析算法 |
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| 引用本文： | 马奎俊,韩彦军,陶卿,王珏.基于核的慢特征分析算法[J].模式识别与人工智能,2011,24(2):153-159. |
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| 作者姓名： | 马奎俊 韩彦军 陶卿 王珏 |
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| 作者单位： | 中国科学院自动化研究所复杂系统与智能科学重点实验室北京100190 |
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| 摘 要： | 提出一种基于核的慢特征分析算法。通过引入核技巧，既充分扩充特征空间，又避免直接在高维空间中运算的困难。由于充分利用数据所隐含的非线性信息，所得到的解是稳定的。同时基于对慢特征分析算法目标函数的分析，给出一个对算法结果的评价准则，并用以指导核参数的选择。实验结果验证算法的有效性。
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| 关 键 词： | 不变量学习 慢特征分析 核方法 盲源信号分离 |
| 收稿时间： | 2010-01-04 |
Kernel-Based Slow Feature Analysis |
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| MA Kui-Jun,HAN Yan-Jun,TAO Qing,WANG Jue.Kernel-Based Slow Feature Analysis[J].Pattern Recognition and Artificial Intelligence,2011,24(2):153-159. |
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| Authors: | MA Kui-Jun HAN Yan-Jun TAO Qing WANG Jue |
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| Affiliation: | Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing 100190 |
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| Abstract: | A kernel-based algorithm is proposed to solve the nonlinear expansion problem of slow feature analysis (SFA).By using the kernel trick,the difficulties of computing directly in high dimensional space are avoided.Because of the full use of nonlinear information of the data,its output is steady.Meanwhile,based on the objective analysis of the proposed algorithm,a formula is put forward to estimate the output slowness of the signal and it is utilized as a guide line to select parameters of the kernel functions.Experimental results show the effectiveness of the proposed algorithm. |
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| Keywords: | Invariance Learning Slow Feature Analysis Kernel Method Blind Source Separation |
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