Theoretical developments for interpreting kernel spectral clustering from alternative viewpoints. Peluffo-Ordóñez, D., Rosero-Montalvo, P., Umaquinga-Criollo, A., Suárez-Zambrano, L., Domínguez-Limaico, H., Oña-Rocha, O., Flores-Armas, S., & Maya-Olalla, E. Advances in Science, Technology and Engineering Systems Journal, 2(3):1670-1676, 8, 2017.
Theoretical developments for interpreting kernel spectral clustering from alternative viewpoints [link]Website  doi  abstract   bibtex   2 downloads  
To perform an exploration process over complex structured data within unsupervised settings, the so-called kernel spectral clustering (KSC) is one of the most recommended and appealing approaches, given its versatility and elegant formulation. In this work, we explore the relationship between (KSC) and other well-known approaches, namely normalized cut clustering and kernel k-means. To do so, we first deduce a generic KSC model from a primal-dual formulation based on least-squares support-vector machines (LS-SVM). For experiments, KSC as well as other consider methods are assessed on image segmentation tasks to prove their usability.
@article{
 title = {Theoretical developments for interpreting kernel spectral clustering from alternative viewpoints},
 type = {article},
 year = {2017},
 keywords = {Kernel,Spectral clustering,Support vector machines},
 pages = {1670-1676},
 volume = {2},
 websites = {http://astesj.com/v02/i03/p208/},
 month = {8},
 id = {7451d6b7-9699-309e-bdb7-b5503ca2512a},
 created = {2022-01-26T03:00:44.144Z},
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 abstract = {To perform an exploration process over complex structured data within unsupervised settings, the so-called kernel spectral clustering (KSC) is one of the most recommended and appealing approaches, given its versatility and elegant formulation. In this work, we explore the relationship between (KSC) and other well-known approaches, namely normalized cut clustering and kernel k-means. To do so, we first deduce a generic KSC model from a primal-dual formulation based on least-squares support-vector machines (LS-SVM). For experiments, KSC as well as other consider methods are assessed on image segmentation tasks to prove their usability.},
 bibtype = {article},
 author = {Peluffo-Ordóñez, Diego and Rosero-Montalvo, Paul and Umaquinga-Criollo, Ana and Suárez-Zambrano, Luis and Domínguez-Limaico, Hernan and Oña-Rocha, Omar and Flores-Armas, Stefany and Maya-Olalla, Edgar},
 doi = {10.25046/aj0203208},
 journal = {Advances in Science, Technology and Engineering Systems Journal},
 number = {3}
}

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