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| Kernel Based Slow Feature Analysis |
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Abstract A kernelbased algorithm is proposed to solve the nonlinear exparsion problem of slow feature analysis (SFA) is proposed to solve this problem. By using the kernel trick, it avoids the difficulties of computing directly in high dimensional space. Because of the full use of nonlinear information of the data, its output is steady. Meanwhile, based on analysis of the objective of the algorithm, a formula is put forward to estimate the output slowness of the signal and utilize it as a guide line to choose parameters of the kernel functions. Experimental results show the effectiveness of the proposed algorithm.
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2011, Vol. 24 
