Spectral Clustering with Jensen-type kernels and their multi-point extensions

Ghoshdastidar, Debarghya and Dukkipati, Ambedkar and Adsul, Ajay P and Vijayan, Aparna S (2014) Spectral Clustering with Jensen-type kernels and their multi-point extensions. In: 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), JUN 23-28, 2014, Columbus, OH, pp. 1472-1477.

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Abstract

Motivated by multi-distribution divergences, which originate in information theory, we propose a notion of `multipoint' kernels, and study their applications. We study a class of kernels based on Jensen type divergences and show that these can be extended to measure similarity among multiple points. We study tensor flattening methods and develop a multi-point (kernel) spectral clustering (MSC) method. We further emphasize on a special case of the proposed kernels, which is a multi-point extension of the linear (dot-product) kernel and show the existence of cubic time tensor flattening algorithm in this case. Finally, we illustrate the usefulness of our contributions using standard data sets and image segmentation tasks.

Item Type:	Conference Proceedings
Series.:	IEEE Conference on Computer Vision and Pattern Recognition
Publisher:	IEEE
Additional Information:	Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	30 Oct 2015 06:45
Last Modified:	30 Oct 2015 06:45
URI:	http://eprints.iisc.ac.in/id/eprint/52658

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