Unsupervised Parallel Feature Extraction from First Principles
Abstract
We describe a number of learning rules that can be used to train un(cid:173) supervised parallel feature extraction systems. The learning rules are derived using gradient ascent of a quality function. We con(cid:173) sider a number of quality functions that are rational functions of higher order moments of the extracted feature values. We show that one system learns the principle components of the correla(cid:173) tion matrix. Principal component analysis systems are usually not optimal feature extractors for classification. Therefore we design quality functions which produce feature vectors that support unsu(cid:173) pervised classification. The properties of the different systems are compared with the help of different artificially designed datasets and a database consisting of all Munsell color spectra.
Cite
Text
Österberg and Lenz. "Unsupervised Parallel Feature Extraction from First Principles." Neural Information Processing Systems, 1993.Markdown
[Österberg and Lenz. "Unsupervised Parallel Feature Extraction from First Principles." Neural Information Processing Systems, 1993.](https://mlanthology.org/neurips/1993/osterberg1993neurips-unsupervised/)BibTeX
@inproceedings{osterberg1993neurips-unsupervised,
title = {{Unsupervised Parallel Feature Extraction from First Principles}},
author = {Österberg, Mats and Lenz, Reiner},
booktitle = {Neural Information Processing Systems},
year = {1993},
pages = {136-143},
url = {https://mlanthology.org/neurips/1993/osterberg1993neurips-unsupervised/}
}