Factorial Learning by Clustering Features
Abstract
We introduce a novel algorithm for factorial learning, motivated by segmentation problems in computational vision, in which the underlying factors correspond to clusters of highly correlated input features. The algorithm derives from a new kind of competitive clustering model, in which the cluster generators compete to ex(cid:173) plain each feature of the data set and cooperate to explain each input example, rather than competing for examples and cooper(cid:173) ating on features, as in traditional clustering algorithms. A natu(cid:173) ral extension of the algorithm recovers hierarchical models of data generated from multiple unknown categories, each with a differ(cid:173) ent, multiple causal structure. Several simulations demonstrate the power of this approach.
Cite
Text
Tenenbaum and Todorov. "Factorial Learning by Clustering Features." Neural Information Processing Systems, 1994.Markdown
[Tenenbaum and Todorov. "Factorial Learning by Clustering Features." Neural Information Processing Systems, 1994.](https://mlanthology.org/neurips/1994/tenenbaum1994neurips-factorial/)BibTeX
@inproceedings{tenenbaum1994neurips-factorial,
title = {{Factorial Learning by Clustering Features}},
author = {Tenenbaum, Joshua B. and Todorov, Emanuel V.},
booktitle = {Neural Information Processing Systems},
year = {1994},
pages = {561-568},
url = {https://mlanthology.org/neurips/1994/tenenbaum1994neurips-factorial/}
}