Sohail Bahmani

Algorithms for Sparsity-Constrained Optimization

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Springer Theses 261
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-319-37719-3
• Softcover reprint of the original 1st ed. 2014
• Seitenzahl: 132
• Erscheinungstermin: 23. August 2016
• Englisch
• Abmessung: 235mm x 155mm x 8mm
• Gewicht: 230g
• ISBN-13: 9783319377193
• ISBN-10: 3319377191
• Artikelnr.: 45457790

Autorenporträt

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

Inhaltsangabe

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.

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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.