Algorithms for Sparsity-Constrained Optimization
Sohail Bahmani
Oct 2013 · Springer Theses Book 261 · Springer Science & Business Media
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About this ebook
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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About the author
Dr. Bahmani completed his thesis at Carnegie Mellon University and is currently employed by the Georgia Institute of Technology.
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