Spectral Clustering with Jensen-type kernels and their multi-point extensions
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.
Keywords
Cite
@article{arxiv.1403.4378,
title = {Spectral Clustering with Jensen-type kernels and their multi-point extensions},
author = {Debarghya Ghoshdastidar and Ambedkar Dukkipati and Ajay P. Adsul and Aparna S. Vijayan},
journal= {arXiv preprint arXiv:1403.4378},
year = {2016}
}
Comments
To appear in IEEE Computer Society Conference on Computer Vision and Pattern Recognition