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Slow feature analysis (SFA) is an unsupervised learning algorithm that extracts slowly varying features from a time series.
Connectionist learning procedures
G. E. Hinton · 1989
Earlier work this paper cites.
Learning invariance from transformation sequences
P. Földiák · 1991
Earlier work this paper cites.
Removing time variation with the anti-Hebbian differential synapse
G. Mitchison · 1991
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Learning invariance manifolds
L. Wiskott · 1998
Earlier work this paper cites.
Slow Feature Analysis: Unsupervised learning of invariances
L. Wiskott and T. Sejnowski · 2002
Earlier work this paper cites.
Laplacian eigenmaps for dimensionality reduction and data representation
M. Belkin and P. Niyogi · 2003
Cited alongside, same era.
Locality Preserving Projections
X. He and P. Niyogi · 2003
Cited alongside, same era.
Slow Feature Analysis: A theoretical analysis of optimal free responses
L. Wiskott · 2003
Cited alongside, same era.
Replacing supervised classification learning by Slow Feature Analysis in spiking neural networks
S. Klampfl and W. Maass · 2010
Cited alongside, same era.
Heuristic evaluation of expansions for non-linear hierarchical Slow Feature Analysis
A. N. Escalante-B. and L. Wiskott · 2011
Cited alongside, same era.
Pattern recognition with Slow Feature Analysis
P. Berkes
Cited in the paper.
Handwritten digit recognition with nonlinear Fisher discriminant analysis
P. Berkes
Cited in the paper.
How to solve classification and regression problems on high-dimensional data with a supervised extension of Slow Feature Analysis
A. N. Escalante-B. and L. Wiskott · 2013
Later among the works it cites.
Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark
S. Houben, J. Stallkamp, J. Salmen, M. Schlipsing, and C. Igel · 2013
Later among the works it cites.
Deep hierarchies in the primate visual cortex: What can we learn for computer vision?
N. Krüger, P. Janssen, S. Kalkan, M. Lappe, A. Leonardis, J. Piater, A. Rodriguez-Sanchez, and L. Wiskott · 2013
Later among the works it cites.
A framework for joint estimation of age, gender and ethnicity on a large database
G. Guo and G. Mu · 2014
Later among the works it cites.
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