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Regularized Sparse Kernel Slow Feature Analysis
Published on Oct 03, 20112878 Views
This paper develops a kernelized slow feature analysis (SFA) algorithm. SFA is an unsupervised learning method to extract features which encode latent variables from time series. Generative relationsh
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Chapter list
Regularized Sparse Kernel Slow Feature Analysis00:00
Linear solutions to non-linear problems (1)00:41
Linear solutions to non-linear problems (2)01:30
Linear solutions to non-linear problems (3)02:36
Unsupervised non-linear feature extraction03:56
RSK-SFA - slow feature analysis05:53
RSK-SFA - kernel slow feature analysis09:48
RSK-SFA - penalizing complex functions12:57
RSK-SFA - preventing complex functions15:09
Feature validation: vowel classification (1)17:40
Feature validation: vowel classification (2)19:21
Take home message21:48
Thank you for your attention!22:19