Algorithms for Sparsity-Constrained Optimization

2014
ISBN: 978-3-319-01880-5
Verlag: Springer International Publishing

Buch, Englisch, Band 261, 107 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 3497 g

Reihe: Springer Theses

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.

Zielgruppe

Research

Autoren/Hrsg.

Bahmani, Sohail

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen

Inhaltsverzeichnis

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.

Über Autor(innen)

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