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Mutual coherence (linear algebra)
Value in matrix theory From Wikipedia, the free encyclopedia
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In linear algebra, mutual coherence (or simply coherence) measures the maximum similarity between any two columns of a matrix, defined as the largest absolute value of their cross-correlations.[1][2] First explored by David Donoho and Xiaoming Huo in the late 1990s for pairs of orthogonal bases,[3] it was later expanded by Donoho and Michael Elad in the early 2000s to study sparse representations[4]—where signals are built from a few key components in a larger set.
In signal processing, mutual coherence is widely used to assess how well algorithms like matching pursuit and basis pursuit can recover a signal’s sparse representation from a collection with extra building blocks, known as an overcomplete dictionary.[1][2][5]
Joel Tropp extended this idea with the Babel function, which applies coherence from one column to a group, equaling mutual coherence for two columns while broadening its use for larger sets with any number of columns.[6]
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Formally, let be the columns of the matrix A, which are assumed to be normalized such that The mutual coherence of A is then defined as[1][2]
A lower bound is[7]
A deterministic matrix with the mutual coherence almost meeting the lower bound can be constructed by Weil's theorem.[8]
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