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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| Published in: | Machine learning Vol. 89; no. 1-2; pp. 67 - 86 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Boston
Springer US
01.10.2012
Springer Nature B.V |
| Subjects: | |
| ISSN: | 0885-6125, 1573-0565 |
| Online Access: | Get full text |
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