Generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis

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...

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Bibliographic Details
Published in:Machine learning Vol. 89; no. 1-2; pp. 67 - 86
Main Authors: Böhmer, Wendelin, Grünewälder, Steffen, Nickisch, Hannes, Obermayer, Klaus
Format: Journal Article
Language:English
Published: Boston Springer US 01.10.2012
Springer Nature B.V
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ISSN:0885-6125, 1573-0565
Online Access:Get full text
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