TY  - BOOK
AU  - Bahmani,Sohail
ED  - SpringerLink (Online service)
TI  - Algorithms for Sparsity-Constrained Optimization
T2  - Springer Theses, Recognizing Outstanding Ph.D. Research,
SN  - 9783319018812
AV  - TK5102.9 
U1  - 621.382 23
PY  - 2014///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - engineering
KW  - Computer vision
KW  - Engineering
KW  - Signal, Image and Speech Processing
KW  - Mathematical Applications in Computer Science
KW  - Image Processing and Computer Vision
N1  - 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
N2  - 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
UR  - http://dx.doi.org/10.1007/978-3-319-01881-2
ER  -