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Algorithms for Sparsity-Constrained Optimization [electronic resource] / by Sohail Bahmani.

By: Bahmani, Sohail [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Springer Theses, Recognizing Outstanding Ph.D. Research: 261Publisher: Cham : Springer International Publishing : Imprint: Springer, 2014Description: XXI, 107 p. 13 illus., 12 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319018812.Subject(s): Engineering | Image processing | Computer science -- Mathematics | Computer mathematics | Engineering | Signal, Image and Speech Processing | Mathematical Applications in Computer Science | Image Processing and Computer VisionAdditional physical formats: Printed edition:: No titleDDC classification: 621.382 Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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.
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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.

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.

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