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" Practical applications of sparse modeling / "
edited by Irina Rish, Guillermo A. Cecchi, Aurelie Lozano, and Alexandru Niculescu-Mizil.
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BL
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| Record Number
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877059
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| Title & Author
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Practical applications of sparse modeling /\ edited by Irina Rish, Guillermo A. Cecchi, Aurelie Lozano, and Alexandru Niculescu-Mizil.
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| Publication Statement
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Cambridge, Massachusetts :: The MIT Press,, [2014]
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| Series Statement
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Neural information processing series
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| Page. NO
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xii, 249 pages :: illustrations (some color) ;; 26 cm.
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| ISBN
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0262027720
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: 9780262027724
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| Bibliographies/Indexes
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Includes bibliographical references and index.
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| Contents
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Chapter 1. Introduction / Irina Rish, Guillermo A. Cecchi, Aurelie Lozano, and Alexandru Niculescu-Mizil -- chapter 2. The challenges of systems biology / Pablo Meyer and Guillermo A. Cecchi -- chapter 3. Practical sparse modeling : an overview and two examples from genetics / Saharon Rosset -- chapter 4. High-dimensional sparse structured input-output models, with applications to GWAS / Eric P. Xing, Mladen Kolar, Seyoung Kim, and Xi Chen -- chapter. 5. Sparse recovery for protein mass spectrometry data / Martin Slawski and Matthias Hein -- chapter 6. Stability and reproducibility in fMRI analysis / Stephen C. Strother, Peter M. Rasmussen, Nathan W. Churchill, and Lars Kai Hansen -- chapter 7. Reliability estimation and enhancement via spatial smoothing in sparse fMRI modeling / Melissa K. Carroll, Guillermo A. Cecchi, Irina Rish, Rahul Garg, Marwan Baliki, and A. Vania Apkarian -- chapter 8. Sequential testing for sparse recovery / Matthew L. Malloy and Robert D. Nowak -- chapter 9. Linear inverse problems with norm and sparsity constraints / Volkan Cevher, Sina Jafarpour, and Anastasios Kyrillidis -- chapter 10. Bayesian approaches for sparse latent variable models : reconsidering L₁ sparsity / Shakir Mohamed, Katherine Heller, and Zoubin Ghahramani -- chapter 11. Sparsity in topic models / Jagannadan Varadarajan, Rémi Emonet, and Jean-Marc Odobez.
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| Abstract
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"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 data sets. This collection describes key approaches in sparse modeling, focusing on its applications in such fields as 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"--Jacket.
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| Subject
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Data reduction.
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Mathematical models.
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Sampling (Statistics)
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Sparse matrices.
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Data reduction.
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Mathematical models.
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Sampling (Statistics)
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Sparse matrices.
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| Dewey Classification
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003/.74
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| LC Classification
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TA342.P73 2014
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| Added Entry
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Rish, Irina,1969-
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