Sparse modeling theory, algorithms, and applications
Sparse models are particularly useful in scientific applications, such as biomarker discovery in genetic or neuroimaging data, where the interpretability of a predictive model is essential. Sparsity can also dramatically improve the cost efficiency of signal processing.Sparse Modeling: Theory, Algor...
| Otros Autores: | , |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Boca Raton, FL :
CRC Press
2015.
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| Edición: | 1st edition |
| Colección: | Chapman & Hall/CRC machine learning & pattern recognition series.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009628759506719 |
Tabla de Contenidos:
- Cover; Contents; List of Figures; Preface; Chapter 1: Introduction; Chapter 2: Sparse Recovery: Problem Formulations; Chapter 3: Theoretical Results (Deterministic Part); Chapter 4: Theoretical Results (Probabilistic Part); Chapter 5: Algorithms for Sparse Recovery Problems; Chapter 6: Beyond LASSO: Structured Sparsity; Chapter 7: Beyond LASSO: Other Loss Functions; Chapter 8: Sparse Graphical Models; Chapter 9: Sparse Matrix Factorization: Dictionary Learning and Beyond; Epilogue; Appendix: Mathematical Background; Bibliography; Color Inserts; Back Cover
