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Generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis
2012
Machine Learning
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,
doi:10.1007/s10994-012-5300-0
fatcat:taybe3sdebggrnjp4xymxhfcga