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

By: Contributor(s): Material type: TextTextSeries: 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 resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783319018812
Subject(s): Additional physical formats: Printed edition:: No titleDDC classification:
  • 621.382 23
LOC classification:
  • TK5102.9
  • TA1637-1638
  • TK7882.S65
Online resources:
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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Item type Current library Call number Status Date due Barcode
E-Book E-Book Central Library Available E-41221

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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