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Engineering - Signals & Communication | Algorithms for Sparsity-Constrained Optimization

Algorithms for Sparsity-Constrained Optimization

Series: Springer Theses, Vol. 261

Bahmani, Sohail

2014, XXI, 107 p. 13 illus., 12 illus. in color.

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  • Nominated by Carnegie Mellon University as an outstanding Ph.D. thesis
  • Provides an new direction of research into problems of extracting structure from data
  • Advances the science of structure discovery through sparsity
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.

Content Level » Research

Keywords » Compressed Sensing - GraSP Algorithm - Linear Models - Linear Regression - Logistic Regression - Model-Based Sparsity - Nonlinear Inference - Smooth Cost Functions

Related subjects » Image Processing - Signals & Communication

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