Different perspectives for kernel spectral clustering: A theoretical study. Peluffo, D., H., Rosero, P., D., Pupiales, C., H., Suarez, L., E., Jaramillo, E., D., Maya, E., A., Michilena, J., R., & Vasquez, C., A. In 2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016, 2016. ![Different perspectives for kernel spectral clustering: A theoretical study [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex 1 download © 2016 IEEE. Spectral clustering is a suitable technique to deal with problems involving unlabeled clusters and having a complex structure, being kernel-based approaches the most recommended ones. This work aims at demonstrating the relationship between a widely-recommended method, so-named kernel spectral clustering (KSC) and other well-known approaches, namely normalized cut clustering and kernel k-means. Such demonstrations are done by following a primal-dual scheme. Also, we mathematically and experimentally prove the usability of using LS-SVM formulations with a model. Experiments are conducted to assess the clustering performance of KSC and the other considered methods on image segmentation tasks.
@inproceedings{
title = {Different perspectives for kernel spectral clustering: A theoretical study},
type = {inproceedings},
year = {2016},
keywords = {Kernel,spectral clustering,support vector machines},
websites = {https://ieeexplore.ieee.org/document/7750849},
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abstract = {© 2016 IEEE. Spectral clustering is a suitable technique to deal with problems involving unlabeled clusters and having a complex structure, being kernel-based approaches the most recommended ones. This work aims at demonstrating the relationship between a widely-recommended method, so-named kernel spectral clustering (KSC) and other well-known approaches, namely normalized cut clustering and kernel k-means. Such demonstrations are done by following a primal-dual scheme. Also, we mathematically and experimentally prove the usability of using LS-SVM formulations with a model. Experiments are conducted to assess the clustering performance of KSC and the other considered methods on image segmentation tasks.},
bibtype = {inproceedings},
author = {Peluffo, D. H. and Rosero, P. D. and Pupiales, C. H. and Suarez, L. E. and Jaramillo, E. D. and Maya, E. A. and Michilena, J. R. and Vasquez, C. A.},
doi = {10.1109/ETCM.2016.7750849},
booktitle = {2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016}
}
Downloads: 1
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