Generalization from Observed to Unobserved Features by Clustering

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Generalization from Observed to Unobserved Features by Clustering (2006)

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by Eyal Krupka , Naftali Tishby

Venue:	JOURNAL OF MACHINE LEARNING RESEARCH
Citations:	2 - 2 self

BibTeX

@MISC{Krupka06generalizationfrom,
    author = {Eyal Krupka and Naftali Tishby},
    title = { Generalization from Observed to Unobserved Features by Clustering},
    year = {2006}
}

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Abstract

We argue that when objects are characterized by many attributes, clustering them on the basis of a 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 finite sample generalization theorems for this novel learning scheme that extends analogous results from the supervised learning setting. We use our framework to analyze generalization to unobserved features of two well-known clustering algorithms: k-means and the maximum likelihood multinomial mixture model. The scheme is demonstrated for collaborative filtering of users with movie ratings as attributes and document clustering with words as attributes.

Keyphrases

unobserved feature machine learning research random subset mild technical condition unobserved attribute analogous result finite sample generalization many attribute maximum likelihood multinomial mixture model full attribute supervised learning collaborative filtering movie rating

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