Algorithms for Sparsity-Constrained Optimization

Algorithms for Sparsity-Constrained Optimization

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 o...

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Bibliographic Details
Published in:Springer eBooks
Main Author: Bahmani, Sohail (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2014.
Series:Springer Theses, Recognizing Outstanding Ph.D. Research,
Subjects:
engineering.
Computer vision.
Engineering.
Signal, Image and Speech Processing.
Mathematical Applications in Computer Science.
Image Processing and Computer Vision.
Online Access:http://dx.doi.org/10.1007/978-3-319-01881-2
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Description
Summary: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.
Physical Description:XXI, 107 p. 13 illus., 12 illus. in color. online resource.
ISBN:9783319018812
ISSN:2190-5053 ;

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