Algorithms for Sparsity-Constrained Optimization

Beschreibung

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.
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.
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 Includes supplementary material: sn.pub/extras

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Sohail Bahmani

Themen in »Algorithms for Sparsity-Constrained Optimization«

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

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