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KDD
2012
ACM

Active spectral clustering via iterative uncertainty reduction

7 years 12 months ago
Active spectral clustering via iterative uncertainty reduction
Spectral clustering is a widely used method for organizing data that only relies on pairwise similarity measurements. This makes its application to non-vectorial data straightforward in principle, as long as all pairwise similarities are available. However, in recent years, numerous examples have emerged in which the cost of assessing similarities is substantial or prohibitive. We propose an active learning algorithm for spectral clustering that incrementally measures only those similarities that are most likely to remove uncertainty in an intermediate clustering solution. In many applications, similarities are not only costly to compute, but also noisy. We extend our algorithm to maintain running estimates of the true similarities, as well as estimates of their accuracy. Using this information, the algorithm updates only those estimates which are relatively inaccurate and whose update would most likely remove clustering uncertainty. We compare our methods on several datasets, includi...
Fabian L. Wauthier, Nebojsa Jojic, Michael I. Jord
Added 28 Sep 2012
Updated 28 Sep 2012
Type Journal
Year 2012
Where KDD
Authors Fabian L. Wauthier, Nebojsa Jojic, Michael I. Jordan
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