Algorithms for Sparsity-Constrained Optimization
(Sprache: Englisch)
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...
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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.
Klappentext zu „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 of the inaccuracies that occurred with the use of previous models.
Inhaltsverzeichnis zu „Algorithms for Sparsity-Constrained Optimization “
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.
Autoren-Porträt von Sohail Bahmani
Dr. Bahmani completed his thesis at Carnegie Mellon University and is currently employed by the Georgia Institute of Technology.
Bibliographische Angaben
- Autor: Sohail Bahmani
- 2013, 2014, XXI, 107 Seiten, 12 farbige Abbildungen, Masse: 16 x 24,1 cm, Gebunden, Englisch
- Verlag: Springer, Berlin
- ISBN-10: 3319018809
- ISBN-13: 9783319018805
- Erscheinungsdatum: 18.10.2013
Sprache:
Englisch
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