Algorithms for Sparsity-Constrained Optimization

ISBN-13: 9783319377193 / Angielski / Miękka / 2016 / 107 str.

Sohail Bahmani

Opis

This thesis presents a wholly new technique in the structural analysis of data that uses a 'greedy' algorithm to derive optimal sparse solutions, enabling faster and more accurate results in formerly problematic areas of machine learning and signal processing.

Spis treści

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.- Appendix A Proofs of Chapter 3.- Appendix B Proofs of Chapter 4.- Appendix C Proofs of Chapter 5.- Appendix D Proofs of Chapter 6.

O autorze

Dr. Bahmani completed his thesis at Carnegie Mellon University and is currently employed by the Georgia Institute of Technology.

Blurb

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.