Algorithms for Sparsity-Constrained Optimization de Sohail Bahmani

Algorithms for Sparsity-Constrained Optimization: Springer Theses, cartea 261

en Limba Engleză Hardback – 18 oct 2013

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.

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Specificații

ISBN-13: 9783319018805
ISBN-10: 3319018809
Pagini: 154
Ilustrații: XXI, 107 p. 13 illus., 12 illus. in color.
Dimensiuni: 155 x 235 x 15 mm
Greutate: 0.39 kg
Ediția:2014
Editura: Springer International Publishing
Colecția Springer
Seria Springer Theses

Locul publicării:Cham, Switzerland

Public țintă

Research

Cuprins

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.

Notă biografică

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

Textul de pe ultima copertă

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.

Caracteristici

Nominated by Carnegie Mellon University as an outstanding Ph.D. thesis Provides an new direction of research into problems of extracting structure from data Advances the science of structure discovery through sparsity Includes supplementary material: sn.pub/extras

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