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Generalized principal component analysis
This book provides a comprehensive introduction to the latest advances in the mathematical theory and computational tools for modeling high-dimensional data drawn from one or multiple low-dimensional subspaces (or manifolds) and potentially corrupted by noise, gross errors, or outliers. This challen...
| Autores principales: | , , |
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| Lenguaje: | eng |
| Publicado: |
Springer
2016
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| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.1007/978-0-387-87811-9 http://cds.cern.ch/record/2151761 |