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Slow Feature Analysis: Unsupervised Learning of Invariances
| Citations: | 240 - 13 self |
BibTeX
@MISC{Wiskott_slowfeature,
author = {Laurenz Wiskott and Terrence J. Sejnowski},
title = { Slow Feature Analysis: Unsupervised Learning of Invariances},
year = {}
}
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Abstract
Invariant features of temporally varying signals are useful for analysis and classification. Slow feature analysis (SFA) is a new method for learning invariant or slowly varying features from a vectorial input signal. It is based on a nonlinear expansion of the input signal and application of principal component analysis to this expanded signal and its time derivative. It is guaranteed to find the optimal solution within a family of functions directly and can learn to extract a large number of decorrelated features, which are ordered by their degree of invariance. SFA can be applied hierarchically to process high-dimensional input signals and extract complex features. SFA is applied first to complex cell tuning properties based on simple cell output, including disparity and motion. Then more complicated input-output functions are learned by repeated application of SFA. Finally, a hierarchical network of SFA modules is presented as a simple model of the visual system. The same unstructured network can learn translation, size, rotation, contrast, or, to a lesser degree, illumination invariance for one-dimensional objects, depending on only the training stimulus. Surprisingly, only a few training objects suffice to achieve good generalization to new objects. The generated representation is suitable for object recognition. Performance degrades if the network is trained to learn multiple invariances simultaneously.
Keyphrases
slow feature analysis unsupervised learning nonlinear expansion complicated input-output function sfa module new method one-dimensional object large number principal component analysis repeated application training stimulus training object decorrelated feature vectorial input signal optimal solution simple cell output hierarchical network visual system simple model invariant feature illumination invariance unstructured network multiple invariance good generalization object recognition input signal high-dimensional input signal extract complex feature new object varying feature generated representation
