On Greedy Algorithms with bounded cumulative coherence
Eugene Livshitz
TL;DR
This paper analyzes the convergence rates of Pure and Orthogonal Greedy Algorithms when applied to dictionaries with bounded cumulative coherence, providing bounds for their performance.
Contribution
It offers new upper and lower estimates for the convergence rates of these greedy algorithms under bounded cumulative coherence conditions.
Findings
Derived bounds for convergence rates
Compared performance of Pure and Orthogonal Greedy Algorithms
Established theoretical limits based on cumulative coherence
Abstract
We discuss the upper and lower estimates for the rate of convergence of Pure and Orthogonal Greedy Algorithms for dictionary with bounded cumulative coherence.
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Taxonomy
TopicsSparse and Compressive Sensing Techniques · Image and Signal Denoising Methods · Blind Source Separation Techniques