Algorithms for Sparsity-Constrained Optimization

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 o...

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Bibliographic Details
Main Author: Bahmani, Sohail (Author)
Corporate Author: SpringerLink (Online service)
Format: Electronic eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2014.
Series:Springer Theses, Recognizing Outstanding Ph.D. Research, 261
Subjects:
Online Access:Full Text via HEAL-Link
Table of 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.