Bahmani, Sohail. <br/><br/>
Algorithms for Sparsity-Constrained Optimization  [electronic resource] / 
by Sohail Bahmani. 
 - XXI, 107 p. 13 illus., 12 illus. in color.  online resource. 
 - Springer Theses, Recognizing Outstanding Ph.D. Research,  261  2190-5053 ; .
 - Springer Theses, Recognizing Outstanding Ph.D. Research,  261 .
<br/><br/>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. 
<br/><br/> 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. 
<br/><br/><label>ISBN: </label>9783319018812 
<br/><br/><label>Standard No.: </label>10.1007/978-3-319-01881-2  doi 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Engineering.<br/>Image processing.<br/>Computer science--Mathematics.<br/>Computer mathematics.<br/>Engineering.<br/>Signal, Image and Speech Processing.<br/>Mathematical Applications in Computer Science.<br/>Image Processing and Computer Vision.
<br/><br/><label>LC Class. No.: </label>TK5102.9 TA1637-1638 TK7882.S65 
<br/><br/><label>Dewey Class. No.: </label>621.382