|Title||Graph Approximation and Clustering on a Budget|
|Publication Type||Conference Proceedings|
|Year of Publication||2015|
|Authors||Fetaya, E, Shamir, O, Ullman, S|
|Conference Name||Artificial Intelligence and Statistics|
We consider the problem of learning from a similarity matrix (such as spectral cluster- ing and low-dimensional embedding), when computing pairwise similarities are costly, and only a limited number of entries can be observed. We provide a theoretical anal- ysis using standard notions of graph ap- proximation, signiﬁcantly generalizing pre- vious results, which focused on spectral clustering with two clusters. We also pro- pose 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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