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ALT
2006
Springer

Unsupervised Slow Subspace-Learning from Stationary Processes

12 years 6 months ago
Unsupervised Slow Subspace-Learning from Stationary Processes
Abstract. We propose a method of unsupervised learning from stationary, vector-valued processes. A low-dimensional subspace is selected on the basis of a criterion which rewards data-variance (like PSA) and penalizes the variance of the velocity vector, thus exploiting the shorttime dependencies of the process. We prove error bounds in terms of the -mixing coe¢ cients and consistency for absolutely regular processes. Experiments with image recognition demonstrate the algorithms ability to learn geometrically invariant feature maps.
Andreas Maurer
Added 14 Mar 2010
Updated 14 Mar 2010
Type Conference
Year 2006
Where ALT
Authors Andreas Maurer
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