Algorithms for Sparsity-Constrained Optimization [electronic resource] /
Bahmani, Sohail.author.
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.
Description based upon print version of record.
Includes bibliographical references.
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.
Signal processing.
Image processing.
Speech processing systems.
Computer science—Mathematics.
Computer mathematics.
Optical data processing.
Signal, Image and Speech Processing.
Mathematical Applications in Computer Science.
Image Processing and Computer Vision.

Springer Theses