Optimisation of on-line principal component analysis
Disordered Systems and Neural Networks
2009-10-31 v1
Abstract
Different techniques, used to optimise on-line principal component analysis, are investigated by methods of statistical mechanics. These include local and global optimisation of node-dependent learning-rates which are shown to be very efficient in speeding up the learning process. They are investigated further for gaining insight into the learning rates' time-dependence, which is then employed for devising simple practical methods to improve training performance. Simulations demonstrate the benefit gained from using the new methods.
Cite
@article{arxiv.cond-mat/9812114,
title = {Optimisation of on-line principal component analysis},
author = {E Schloesser and D Saad and M Biehl},
journal= {arXiv preprint arXiv:cond-mat/9812114},
year = {2009}
}
Comments
10 pages, 5 figures