Algorithms for Sparsity-Constrained Optimization (Springer Theses)

Bahmani, Sohail

Springer International Publishing AG（2016/08発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 107 p.
• 商品コード 9783319377193

Full Description

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.

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.