Corpus ID: 3944763

On the numeric stability of the SFA implementation sfa-tk

@article{Konen2009OnTN,
  title={On the numeric stability of the SFA implementation sfa-tk},
  author={Wolfgang Konen},
  journal={arXiv: Machine Learning},
  year={2009}
}
  • W. Konen
  • Published 6 December 2009
  • Mathematics
  • arXiv: Machine Learning
Slow feature analysis (SFA) is a method for extracting slowly varying features from a quickly varying multidimensional signal. An open source Matlab-implementation sfa-tk makes SFA easily useable. We show here that under certain circumstances, namely when the covariance matrix of the nonlinearly expanded data does not have full rank, this implementation runs into numerical instabilities. We propse a modied algorithm based on singular value decomposition (SVD) which is free of those… Expand

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Slow feature analysis (SFA) is a new unsupervised algorithm to learn nonlinear functions that extract slowly varying signals out of the input data. In this paper we describe its application toExpand
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Slow feature analysis (SFA) is a method for extracting slowly varying driving forces from quickly varying nonstationary time series. We show here that it is possible for SFA to detect a componentExpand
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