Algorithms for Sparsity-Constrained Optimization

This thesis presents a wholly new technique in the structural analysis of data that uses a 'greedy' algorithm to derive optimal sparse solutions, enabling faster and more accurate results in formerly problematic areas of machine learning and signal processing.

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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.