Learning invariance from transformation sequences
Neural Computation
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Jacobi Angles for Simultaneous Diagonalization
SIAM Journal on Matrix Analysis and Applications
Slow feature analysis: unsupervised learning of invariances
Neural Computation
Second-order blind source separation in the Fourier space of data
Signal Processing
Slow feature analysis: a theoretical analysis of optimal free responses
Neural Computation
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Blind separation of mixture of independent sources through aquasi-maximum likelihood approach
IEEE Transactions on Signal Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Independent Slow Feature Analysis and Nonlinear Blind Source Separation
Neural Computation
A Maximum-Likelihood Interpretation for Slow Feature Analysis
Neural Computation
On blind separability based on the temporal predictability method
Neural Computation
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
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We present an analytical comparison between linear slow feature analysis and second-order independent component analysis, and show that in the case of one time delay, the two approaches are equivalent. We also consider the case of several time delays and discuss two possible extensions of slow feature analysis.