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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| TK7882.S65 Speech Dereverberation | TK7882.S65 Computer Vision Analysis of Image Motion by Variational Methods | TK7882.S65 Python for Signal Processing | TK7882.S65 Algorithms for Sparsity-Constrained Optimization | TK7882.S65 Application of Wavelets in Speech Processing | TK7882.S65 Measuring Signal Generators | TK7882.S65 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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