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.

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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}
}