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Algorithms for Sparsity-Constrained Optimization
This thesis demonstrates techniques that provide faster and more accurate solutions to a variety of problems in machine learning and signal processing. The author proposes a"greedy" algorithm, deriving sparse solutions with guarantees of optimality. The use of this algorithm removes many o...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2014.
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| Edición: | 1st ed. 2014. |
| Colección: | Springer Theses, Recognizing Outstanding Ph.D. Research,
261 |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 020 | |a 9783319018812 |9 978-3-319-01881-2 | ||
| 024 | 7 | |a 10.1007/978-3-319-01881-2 |2 doi | |
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| 100 | 1 | |a Bahmani, Sohail. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Algorithms for Sparsity-Constrained Optimization |h [electronic resource] / |c by Sohail Bahmani. |
| 250 | |a 1st ed. 2014. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2014. | |
| 300 | |a XXI, 107 p. 13 illus., 12 illus. in color. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Springer Theses, Recognizing Outstanding Ph.D. Research, |x 2190-5061 ; |v 261 | |
| 505 | 0 | |a Introduction -- Preliminaries -- Sparsity-Constrained Optimization -- Background -- 1-bit Compressed Sensing -- Estimation Under Model-Based Sparsity -- Projected Gradient Descent for `p-constrained Least Squares -- Conclusion and Future Work. | |
| 520 | |a This thesis demonstrates techniques that provide faster and more accurate solutions to a variety of problems in machine learning and signal processing. The author proposes a"greedy" algorithm, deriving sparse solutions with guarantees of optimality. The use of this algorithm removes many of the inaccuracies that occurred with the use of previous models. | ||
| 650 | 0 | |a Signal processing. | |
| 650 | 0 | |a Computer science-Mathematics. | |
| 650 | 0 | |a Computer vision. | |
| 650 | 1 | 4 | |a Signal, Speech and Image Processing . |
| 650 | 2 | 4 | |a Mathematical Applications in Computer Science. |
| 650 | 2 | 4 | |a Computer Vision. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783319018829 |
| 776 | 0 | 8 | |i Printed edition: |z 9783319018805 |
| 776 | 0 | 8 | |i Printed edition: |z 9783319377193 |
| 830 | 0 | |a Springer Theses, Recognizing Outstanding Ph.D. Research, |x 2190-5061 ; |v 261 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-319-01881-2 |z Texto Completo |
| 912 | |a ZDB-2-ENG | ||
| 912 | |a ZDB-2-SXE | ||
| 950 | |a Engineering (SpringerNature-11647) | ||
| 950 | |a Engineering (R0) (SpringerNature-43712) | ||