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基于核的慢特征分析算法 被引量:8

Kernel-Based Slow Feature Analysis
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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. A kernel-based algorithm is proposed analysis (SFA). By using the kernel trick, to solve the nonlinear expansion problem of slow feature the difficulties of computing directly in high dimensional space are avoided. Because of the full use of nonlinear information of the Meanwhile, based on the objective analysis of the proposed algorithm, a estimate the output slowness of the signal and it is utilized as a guide line kernel functions. Experimental results show the effectiveness of the proposed data, its output is steady. formula is put forward to to select parameters of the algorithm.
出处 《模式识别与人工智能》 EI CSCD 北大核心 2011年第2期153-159,共7页 Pattern Recognition and Artificial Intelligence
关键词 不变量学习 慢特征分析 核方法 盲源信号分离 Invariance Learning, Slow Feature Analysis, Kernel Method, Blind Source Separation
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参考文献15

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