Algorithms for Sparsity-Constrained Optimization
Başlık:
Algorithms for Sparsity-Constrained Optimization
Yazar:
Bahmani, Sohail. author.
ISBN:
9783319018812
Ek Yazar:
Fiziksel Tanım:
XXI, 107 p. 13 illus., 12 illus. in color. online resource.
Series:
Springer Theses, Recognizing Outstanding Ph.D. Research, 261
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.
Abstract:
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.
Added Corporate Author:
Electronic Access:
http://dx.doi.org/10.1007/978-3-319-01881-2