Convex Formulation for Kernel PCA and its Use in Semi-Supervised Learning
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Universidad Autónoma de Madrid |
| Repositorio: | Biblos-e Archivo. Repositorio Institucional de la UAM |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.uam.es:10486/710057 |
| Acceso en línea: | http://hdl.handle.net/10486/710057 https://dx.doi.org/10.1109/TNNLS.2017.2709838 |
| Access Level: | acceso abierto |
| Palabra clave: | Kernel principal component analysis (KPCA) kernel spectral clustering semisupervised learning support vector machines Informática |
| Sumario: | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
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