基于核的慢特征分析算法

Kernel-Based Slow Feature Analysis

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.