Theoretical Analysis of the Optimal Free Responses of Graph-Based SFA for the Design of Training Graphs
Abstract
Slow feature analysis (SFA) is an unsupervised learning algorithm that extracts slowly varying features from a multi- dimensional time series. Graph-based SFA (GSFA) is an extension to SFA for supervised learning that can be used to successfully solve regression problems if combined with a simple supervised post-processing step on a small number of slow features. The objective function of GSFA minimizes the squared output differences between pairs of samples specified by the edges of a structure called training graph. The edges of current training graphs, however, are derived only from the relative order of the labels. Exploiting the exact numerical value of the labels enables further improvements in label estimation accuracy.
Cite
Text
Escalante-B. and Wiskott. "Theoretical Analysis of the Optimal Free Responses of Graph-Based SFA for the Design of Training Graphs." Journal of Machine Learning Research, 2016.Markdown
[Escalante-B. and Wiskott. "Theoretical Analysis of the Optimal Free Responses of Graph-Based SFA for the Design of Training Graphs." Journal of Machine Learning Research, 2016.](https://mlanthology.org/jmlr/2016/escalanteb2016jmlr-theoretical/)BibTeX
@article{escalanteb2016jmlr-theoretical,
title = {{Theoretical Analysis of the Optimal Free Responses of Graph-Based SFA for the Design of Training Graphs}},
author = {Escalante-B., Alberto N. and Wiskott, Laurenz},
journal = {Journal of Machine Learning Research},
year = {2016},
pages = {1-36},
volume = {17},
url = {https://mlanthology.org/jmlr/2016/escalanteb2016jmlr-theoretical/}
}