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Multi-label dimensionality reduction /
Similar to other data mining and machine learning tasks, multi-label learning suffers from dimensionality. An effective way to mitigate this problem is through dimensionality reduction, which extracts a small number of features by removing irrelevant, redundant, and noisy information. The data minin...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boca Raton, Florida :
CRC Press,
[2014]
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| Colección: | Chapman & Hall/CRC machine learning & pattern recognition series.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Series; Contents; Preface; Symbol Description; Chapter 1: Introduction; Chapter 2: Partial Least Squares; Chapter 3: Canonical Correlation Analysis; Chapter 4: Hypergraph Spectral Learning; Chapter 5: A Scalable Two-Stage Approach for Dimensionality Reduction; Chapter 6: A Shared-Subspace Learning Framework; Chapter 7: Joint Dimensionality Reduction and Classification; Chapter 8: Nonlinear Dimensionality Reduction: Algorithms and Applications; Appendix Proofs; References; Back Cover.