Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain
Abstract
We present the information-theoretic derivation of a learning algorithm that clusters unlabelled data with linear discriminants. In contrast to methods that try to preserve information about the input patterns, we maximize the information gained from observing the output of robust binary discriminators implemented with sigmoid nodes. We deri ve a local weight adaptation rule via gradient ascent in this objective, demonstrate its dynamics on some simple data sets, relate our approach to previous work and suggest directions in which it may be extended.
Cite
Text
Schraudolph and Sejnowski. "Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain." Neural Information Processing Systems, 1992.Markdown
[Schraudolph and Sejnowski. "Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain." Neural Information Processing Systems, 1992.](https://mlanthology.org/neurips/1992/schraudolph1992neurips-unsupervised/)BibTeX
@inproceedings{schraudolph1992neurips-unsupervised,
title = {{Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain}},
author = {Schraudolph, Nicol N. and Sejnowski, Terrence J.},
booktitle = {Neural Information Processing Systems},
year = {1992},
pages = {499-506},
url = {https://mlanthology.org/neurips/1992/schraudolph1992neurips-unsupervised/}
}