Algorithms for Sparsity-Constrained Optimization [electronic resource] /by Sohail Bahmani.
by Bahmani, Sohail [author.]; SpringerLink (Online service).
Material type:
BookSeries: Springer Theses, Recognizing Outstanding Ph.D. Research: 261Publisher: Cham : Springer International Publishing : 2014.Description: XXI, 107 p. 13 illus., 12 illus. in color. online resource.ISBN: 9783319018812.Subject(s): Engineering | Computer vision | Engineering | Signal, Image and Speech Processing | Mathematical Applications in Computer Science | Image Processing and Computer VisionDDC classification: 621.382 Online resources: Click here to access online
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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.
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.
| Item type | Current location | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
| TA1637-1638 (Browse shelf) | Available | ||||
| TK7882.S65 (Browse shelf) | Available | ||||
| Long Loan | MAIN LIBRARY | TK5102.9 (Browse shelf) | Available |
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| TK5102.9 Speech Dereverberation | TK5102.9 Computer Vision Analysis of Image Motion by Variational Methods | TK5102.9 Python for Signal Processing | TK5102.9 Algorithms for Sparsity-Constrained Optimization | TK5102.9 Application of Wavelets in Speech Processing | TK5102.9 Measuring Signal Generators | TK5102.9 Speech Processing in Mobile Environments |
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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