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Self-Tuning Spectral Clustering

Zelnik-Manor, Lihi and Perona, Pietro (2005) Self-Tuning Spectral Clustering. In: Advances in Neural Information Processing Systems 17 (NIPS 2004). Advances in Neural Information Processing Systems. No.17. MIT Press , Cambridge, MA, pp. 1601-1608. ISBN 0-262-19534-8.

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We study a number of open issues in spectral clustering: (i) Selecting the appropriate scale of analysis, (ii) Handling multi-scale data, (iii) Clustering with irregular background clutter, and, (iv) Finding automatically the number of groups. We first propose that a ‘local’ scale should be used to compute the affinity between each pair of points. This local scaling leads to better clustering especially when the data includes multiple scales and when the clusters are placed within a cluttered background. We further suggest exploiting the structure of the eigenvectors to infer automatically the number of groups. This leads to a new algorithm in which the final randomly initialized k-means stage is eliminated.

Item Type:Book Section
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URLURL TypeDescription
Perona, Pietro0000-0002-7583-5809
Additional Information:© 2005 Massachusetts Institute of Technology. Finally, we wish to thank Yair Weiss for providing us his code for spectral clustering. This research was supported by the MURI award number SA3318 and by the Center of Neuromorphic Systems Engineering award number EEC-9402726.
Funding AgencyGrant Number
Multidisciplinary University Research Initiative (MURI)SA3318
Center for Neuromorphic Systems Engineering, CaltechUNSPECIFIED
Series Name:Advances in Neural Information Processing Systems
Issue or Number:17
Record Number:CaltechAUTHORS:20160314-152424746
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:65341
Deposited By: Kristin Buxton
Deposited On:14 Mar 2016 23:50
Last Modified:03 Oct 2019 09:46

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