Practical applications of sparse modeling
"Sparse modeling is a rapidly developing area at the intersection of statistical learning and signal processing, motivated by the age-old statistical problem of selecting a small number of predictive variables in high-dimensional datasets. This collection describes key approaches in sparse mode...
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| Format: | E-Book |
| Sprache: | English |
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Cambridge, Massachusetts
The MIT Press
[2014]
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| Schriftenreihe: | Neural information processing series
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| 245 | 0 | 0 | |a Practical applications of sparse modeling |c edited by Irina Rish, Guillermo A. Cecchi, Aurelie Lozano, and Alexandru Niculescu-Mizil |
| 264 | 1 | |a Cambridge, Massachusetts |b The MIT Press |c [2014] | |
| 264 | 4 | |c ©2014 | |
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| 490 | 1 | |a Neural information processing series | |
| 520 | |a "Sparse modeling is a rapidly developing area at the intersection of statistical learning and signal processing, motivated by the age-old statistical problem of selecting a small number of predictive variables in high-dimensional datasets. This collection describes key approaches in sparse modeling, focusing on its applications in fields including neuroscience, computational biology, and computer vision. Sparse modeling methods can improve the interpretability of predictive models and aid efficient recovery of high-dimensional unobserved signals from a limited number of measurements. Yet despite significant advances in the field, a number of open issues remain when sparse modeling meets real-life applications. The book discusses a range of practical applications and state-of-the-art approaches for tackling the challenges presented by these applications. Topics considered include the choice of method in genomics applications; analysis of protein mass-spectrometry data; the stability of sparse models in brain imaging applications; sequential testing approaches; algorithmic aspects of sparse recovery; and learning sparse latent models"--MIT CogNet. | ||
| 700 | 1 | |a Cecchi, Guillermo A. | |
| 700 | 1 | |a Lozano, Aurélie Chloé |d 1975- | |
| 700 | 1 | |a Niculescu-Mizil, Alexandru | |
| 700 | 1 | |a Rish, Irina |d 1969- | |
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| indexdate | 2024-12-18T09:01:14Z |
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| publisher | The MIT Press |
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| series2 | Neural information processing series |
| spelling | Practical applications of sparse modeling edited by Irina Rish, Guillermo A. Cecchi, Aurelie Lozano, and Alexandru Niculescu-Mizil Cambridge, Massachusetts The MIT Press [2014] ©2014 1 Online-Ressource (xii, 249 Seiten) txt c cr Neural information processing series "Sparse modeling is a rapidly developing area at the intersection of statistical learning and signal processing, motivated by the age-old statistical problem of selecting a small number of predictive variables in high-dimensional datasets. This collection describes key approaches in sparse modeling, focusing on its applications in fields including neuroscience, computational biology, and computer vision. Sparse modeling methods can improve the interpretability of predictive models and aid efficient recovery of high-dimensional unobserved signals from a limited number of measurements. Yet despite significant advances in the field, a number of open issues remain when sparse modeling meets real-life applications. The book discusses a range of practical applications and state-of-the-art approaches for tackling the challenges presented by these applications. Topics considered include the choice of method in genomics applications; analysis of protein mass-spectrometry data; the stability of sparse models in brain imaging applications; sequential testing approaches; algorithmic aspects of sparse recovery; and learning sparse latent models"--MIT CogNet. Cecchi, Guillermo A. Lozano, Aurélie Chloé 1975- Niculescu-Mizil, Alexandru Rish, Irina 1969- Erscheint auch als Druck-Ausgabe 0262027720 Erscheint auch als Druck-Ausgabe 9780262027724 TUM01 ZDB-260-MPOB TUM_PDA_MPOB MIT Press https://doi.org/10.7551/mitpress/9333.001.0001?locatt=mode:legacy Volltext |
| spellingShingle | Practical applications of sparse modeling |
| title | Practical applications of sparse modeling |
| title_auth | Practical applications of sparse modeling |
| title_exact_search | Practical applications of sparse modeling |
| title_full | Practical applications of sparse modeling edited by Irina Rish, Guillermo A. Cecchi, Aurelie Lozano, and Alexandru Niculescu-Mizil |
| title_fullStr | Practical applications of sparse modeling edited by Irina Rish, Guillermo A. Cecchi, Aurelie Lozano, and Alexandru Niculescu-Mizil |
| title_full_unstemmed | Practical applications of sparse modeling edited by Irina Rish, Guillermo A. Cecchi, Aurelie Lozano, and Alexandru Niculescu-Mizil |
| title_short | Practical applications of sparse modeling |
| title_sort | practical applications of sparse modeling |
| url | https://doi.org/10.7551/mitpress/9333.001.0001?locatt=mode:legacy |
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