Graph Approximation and Clustering on a Budget

Ethan Fetaya; Ohad Shamir; Shimon Ullman

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arXiv · 1406.2602

Graph Approximation and Clustering on a Budget

Ethan Fetaya·Ohad Shamir·Shimon Ullman

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Abstract

We consider the problem of learning from a similarity matrix (such as spectral clustering and lowd imensional embedding), when computing pairwise similarities are costly, and only a limited number of entries can be observed. We provide a theoretical analysis using standard notions of graph approximation, significantly generalizing previous results (which focused on spectral clustering with two clusters). We also propose a new algorithmic approach based on adaptive sampling, which experimentally matches or improves on previous methods, while being considerably more general and computationally cheaper.

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stat.ML cs.AI cs.CV cs.LG

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BibTeX RIS

Ethan Fetaya, Ohad Shamir, Shimon Ullman. 2014-06-10. Graph Approximation and Clustering on a Budget. https://arxiv.org/abs/1406.2602

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