Spectral Clustering with Jensen-type Kernels and their Multi-point Extensions

Debarghya Ghoshdastidar, Ambedkar Dukkipati, Ajay P. Adsul, Aparna S. Vijayan; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 1472-1477

Abstract

Motivated by multi-distribution divergences, which originate in information theory, we propose a notion of `multi-point' 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.

Related Material

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[bibtex]
@InProceedings{Ghoshdastidar_2014_CVPR,
author = {Ghoshdastidar, Debarghya and Dukkipati, Ambedkar and Adsul, Ajay P. and Vijayan, Aparna S.},
title = {Spectral Clustering with Jensen-type Kernels and their Multi-point Extensions},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2014}
}