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Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening
lib:9c7b8a4b8fa4c1cb (v1.0.0)
Authors: Merlin Schüler,Hlynur Davíð Hlynsson,Laurenz Wiskott
ArXiv: 1808.08833
Document: PDF DOI
Abstract URL: https://arxiv.org/abs/1808.08833v3
We propose Power Slow Feature Analysis, a gradient-based method to extract temporally slow features from a high-dimensional input stream that varies on a faster time-scale, as a variant of Slow Feature Analysis (SFA) that allows end-to-end training of arbitrary differentiable architectures and thereby significantly extends the class of models that can effectively be used for slow feature extraction. We provide experimental evidence that PowerSFA is able to extract meaningful and informative low-dimensional features in the case of (a) synthetic low-dimensional data, (b) ego-visual data, and also for (c) a general dataset for which symmetric non-temporal similarities between points can be defined.
ArXiv: 1808.08833
Document: PDF DOI
Abstract URL: https://arxiv.org/abs/1808.08833v3
We propose Power Slow Feature Analysis, a gradient-based method to extract temporally slow features from a high-dimensional input stream that varies on a faster time-scale, as a variant of Slow Feature Analysis (SFA) that allows end-to-end training of arbitrary differentiable architectures and thereby significantly extends the class of models that can effectively be used for slow feature extraction. We provide experimental evidence that PowerSFA is able to extract meaningful and informative low-dimensional features in the case of (a) synthetic low-dimensional data, (b) ego-visual data, and also for (c) a general dataset for which symmetric non-temporal similarities between points can be defined.
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