Spectral Clustering with Jensen-Type Kernels and Their Multi-Point Extensions

Debarghya Ghoshdastidar, Ambedkar Dukkipati, Ajay P. Adsul, Aparna S. Vijayan

CVPR 2014

doi:10.1109/CVPR.2014.191 /cvpr/2014/ghoshdastidar2014cvpr-spectral/

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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Text

Ghoshdastidar et al. "Spectral Clustering with Jensen-Type Kernels and Their Multi-Point Extensions." Conference on Computer Vision and Pattern Recognition, 2014. doi:10.1109/CVPR.2014.191

Markdown

[Ghoshdastidar et al. "Spectral Clustering with Jensen-Type Kernels and Their Multi-Point Extensions." Conference on Computer Vision and Pattern Recognition, 2014.](https://mlanthology.org/cvpr/2014/ghoshdastidar2014cvpr-spectral/) doi:10.1109/CVPR.2014.191

BibTeX

@inproceedings{ghoshdastidar2014cvpr-spectral,
  title     = {{Spectral Clustering with Jensen-Type Kernels and Their Multi-Point Extensions}},
  author    = {Ghoshdastidar, Debarghya and Dukkipati, Ambedkar and Adsul, Ajay P. and Vijayan, Aparna S.},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2014},
  doi       = {10.1109/CVPR.2014.191},
  url       = {https://mlanthology.org/cvpr/2014/ghoshdastidar2014cvpr-spectral/}
}