| Description |
1 online resource (xviii, 231 pages) : illustrations (some color) |
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data file |
| Series |
Chapman & Hall/CRC machine learning & pattern recognition series |
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Chapman & Hall/CRC machine learning & pattern recognition series.
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| Bibliography |
Includes bibliographical references. |
| Contents |
1. Introduction -- 2. Sparse recovery : problem formulations -- 3. Theoretical results (deterministic part) -- 4. Theoretical results (probabilistic part) -- 5. Algorithms for sparse recovery problems -- 6. Beyond LASSO : structured sparsity -- 7. Beyond LASSO : other loss functions -- 8. Sparse graphical models -- 9. Sparse matrix factorization : dictionary learning and beyond. |
| Summary |
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, Algorithms, and Applications provides an introduction to the growing field of sparse modeling, including application examples, problem formulations that yield sparse solutions, algorithms for finding such solutions, and recent theoretical results on sparse recovery. |
| Subject |
Mathematical models.
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Sampling (Statistics)
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Data reduction.
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Sparse matrices.
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Modèles mathématiques. |
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Échantillonnage (Statistique) |
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Réduction des données (Statistique) |
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Matrices éparses. |
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mathematical models. |
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Data reduction |
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Mathematical models |
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Sampling (Statistics) |
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Sparse matrices |
| Added Author |
Grabarnik, Genady Ya., author.
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| Other Form: |
Print version: Rish, Irina, 1969- Sparse modeling. Boca Raton, FL : CRC Press : Taylor & Francis Group, 2015 9781439828694 (OCoLC)902837893 |
| ISBN |
9781439828700 (electronic bk.) |
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1439828709 (electronic bk.) |
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1322667411 (ebk) |
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9781322667416 (ebk) |
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1439828695 |
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9781439828694 |
| Standard No. |
10.1201/b17758 doi |
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