Theoretical developments for interpreting kernel spectral clustering from alternative viewpoints
| Autor: | Diego Hernán Peluffo-Ordóñez, Edgar Maya-Olalla, Ana C. Umaquinga-Criollo, Luis Suárez-Zambrano, Omar R. Ona-Rocha, Stefany Flores-Armas, Hernán Domínguez-Limaico, Paul D. Rosero-Montalvo |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Physics and Astronomy (miscellaneous)
business.industry Computer science Kernel spectral clustering Spectral Clustering lcsh:T Pattern recognition 02 engineering and technology Viewpoints lcsh:Technology Kernel 020204 information systems Management of Technology and Innovation Support Vector Machines 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing lcsh:Q Artificial intelligence business lcsh:Science Engineering (miscellaneous) |
| Zdroj: | Advances in Science, Technology and Engineering Systems, Vol 2, Iss 3, Pp 1670-1676 (2017) |
| ISSN: | 2415-6698 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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