Generalization in Clustering with Unobserved Features
Abstract
We argue that when objects are characterized by many attributes, clustering them on the basis of a relatively small random subset of these attributes can capture information on the unobserved attributes as well. Moreover, we show that under mild technical conditions, clustering the objects on the basis of such a random subset performs almost as well as clustering with the full attribute set. We prove a finite sample generalization theorems for this novel learning scheme that extends analogous results from the supervised learning setting. The scheme is demonstrated for collaborative filtering of users with movies rating as attributes.
Cite
Text
Krupka and Tishby. "Generalization in Clustering with Unobserved Features." Neural Information Processing Systems, 2005.Markdown
[Krupka and Tishby. "Generalization in Clustering with Unobserved Features." Neural Information Processing Systems, 2005.](https://mlanthology.org/neurips/2005/krupka2005neurips-generalization/)BibTeX
@inproceedings{krupka2005neurips-generalization,
title = {{Generalization in Clustering with Unobserved Features}},
author = {Krupka, Eyal and Tishby, Naftali},
booktitle = {Neural Information Processing Systems},
year = {2005},
pages = {683-690},
url = {https://mlanthology.org/neurips/2005/krupka2005neurips-generalization/}
}