Graph approximation and clustering on a budget

Ethan Fetaya, Ohad Shamir, Shimon Ullman

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

We consider the problem of learning from a similarity matrix (such as spectral clustering and low-dimensional 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.

Original languageEnglish
Pages (from-to)241-249
Number of pages9
JournalJournal of Machine Learning Research
Volume38
StatePublished - 2015
Externally publishedYes
Event18th International Conference on Artificial Intelligence and Statistics, AISTATS 2015 - San Diego, United States
Duration: 9 May 201512 May 2015

Bibliographical note

Publisher Copyright:
Copyright 2015 by the authors.

Funding

FundersFunder number
Israel Science Foundation425/13

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