• generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis

    جزئیات بیشتر مقاله
    • تاریخ ارائه: 1392/07/24
    • تاریخ انتشار در تی پی بین: 1392/07/24
    • تعداد بازدید: 908
    • تعداد پرسش و پاسخ ها: 0
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     without non-linear basis functions many problems can not be solved by linear algorithms. this article proposes a method to automatically construct such basis functions with slow feature analysis(sfa). non-linear optimization of this unsupervised learning method generates an orthogonal basis on the unknown latent space for a given time series. in contrast to methods like pca, sfa is thus well suited for techniques that make direct use of the latent space. real-world time series can be complex, and current sfa algorithms are either not powerful enough or tend to over-fit. we make use of the kernel trick in combination with sparsification to develop a kernelized sfa algorithm which provides a powerful function class for large data sets. sparsity is achieved by a novel matching pursuit approach that can be applied to other tasks as well. for small data sets, however, the kernel sfa approach leads to over-fitting and numerical instabilities. to enforce a stable solution, we introduce regularization to the sfa objective. we hypothesize that our algorithm generates a feature space that resembles a fourier basis in the unknown space of latent variables underlying a given real-world time series. we evaluate this hypothesis at the example of a vowel classification task in comparison to sparse kernel pca. our results show excellent classification accuracy and demonstrate the superiority of kernel sfa over kernel pca in encoding latent variables.

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