Kernel spectral clustering for dynamic data using multiple kernel learning. Peluffo-Ordonez, D., Garcia-Vega, S., Langone, R., Suykens, J., A., K., & Castellanos-Dominguez, G. In The 2013 International Joint Conference on Neural Networks (IJCNN), pages 1-6, 8, 2013. IEEE. ![Kernel spectral clustering for dynamic data using multiple kernel learning [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex In this paper we propose a kernel spectral clustering-based technique to catch the different regimes experienced by a time-varying system. Our method is based on a multiple kernel learning approach, which is a linear combination of kernels. The calculation of the linear combination coefficients is done by determining a ranking vector that quantifies the overall dynamical behavior of the analyzed data sequence over-time. This vector can be calculated from the eigenvectors provided by the the solution of the kernel spectral clustering problem. We apply the proposed technique to a trial from the Graphics Lab Motion Capture Database from Carnegie Mellon University, as well as to a synthetic example, namely three moving Gaussian clouds. For comparison purposes, some conventional spectral clustering techniques are also considered, namely, kernel k-means and min-cuts. Also, standard k-means. The normalized mutual information and adjusted random index metrics are used to quantify the clustering performance. Results show the usefulness of proposed technique to track dynamic data, even being able to detect hidden objects. © 2013 IEEE.
@inproceedings{
title = {Kernel spectral clustering for dynamic data using multiple kernel learning},
type = {inproceedings},
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abstract = {In this paper we propose a kernel spectral clustering-based technique to catch the different regimes experienced by a time-varying system. Our method is based on a multiple kernel learning approach, which is a linear combination of kernels. The calculation of the linear combination coefficients is done by determining a ranking vector that quantifies the overall dynamical behavior of the analyzed data sequence over-time. This vector can be calculated from the eigenvectors provided by the the solution of the kernel spectral clustering problem. We apply the proposed technique to a trial from the Graphics Lab Motion Capture Database from Carnegie Mellon University, as well as to a synthetic example, namely three moving Gaussian clouds. For comparison purposes, some conventional spectral clustering techniques are also considered, namely, kernel k-means and min-cuts. Also, standard k-means. The normalized mutual information and adjusted random index metrics are used to quantify the clustering performance. Results show the usefulness of proposed technique to track dynamic data, even being able to detect hidden objects. © 2013 IEEE.},
bibtype = {inproceedings},
author = {Peluffo-Ordonez, D. and Garcia-Vega, S. and Langone, R. and Suykens, J. A. K. and Castellanos-Dominguez, G.},
doi = {10.1109/IJCNN.2013.6706858},
booktitle = {The 2013 International Joint Conference on Neural Networks (IJCNN)}
}
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