Mutual coherence (linear algebra)

In linear algebra, the coherence or mutual coherence of a matrix A is defined as the maximum absolute value of the cross-correlations between the columns of A.[1][2]

Formally, let ${\displaystyle a_{1},\ldots ,a_{m}\in {\mathbb {C} }^{d}}$ be the columns of the matrix A, which are assumed to be normalized such that ${\displaystyle a_{i}^{H}a_{i}=1.}$ The mutual coherence of A is then defined as[1][2]

${\displaystyle M=\max _{1\leq i\neq j\leq m}\left|a_{i}^{H}a_{j}\right|.}$

A lower bound is[3]

${\displaystyle M\geq {\sqrt {\frac {m-d}{d(m-1)}}}.}$

A deterministic matrix with the mutual coherence almost meeting the lower bound can be constructed by Weil's theorem.[4]

This concept was reintroduced by David Donoho and Michael Elad in the context of sparse representations.[5] A special case of this definition for the two-ortho case appeared earlier in the paper by Donoho and Huo.[6] The mutual coherence has since been used extensively in the field of sparse representations of signals. In particular, it is used as a measure of the ability of suboptimal algorithms such as matching pursuit and basis pursuit to correctly identify the true representation of a sparse signal.[1][2][7] Joel Tropp introduced a useful extension of Mutual Coherence, known as the Babel function, which extends the idea of cross-correlation between pairs of columns to the cross-correlation from one column to a set of other columns. The Babel function for two columns is exactly the Mutual coherence, but it also extends the coherence relationship concept in a way that is useful and relevant for any number of columns in the sparse representation matrix as well.[8]

See also

• Compressed sensing
• Restricted isometry property
• Babel function

References

1. Tropp, J.A. (March 2006). "Just relax: Convex programming methods for identifying sparse signals in noise" (PDF). IEEE Transactions on Information Theory. 52 (3): 1030–1051. doi:10.1109/TIT.2005.864420. S2CID 6496872.
2. Donoho, D.L.; M. Elad; V.N. Temlyakov (January 2006). "Stable recovery of sparse overcomplete representations in the presence of noise". IEEE Transactions on Information Theory. 52 (1): 6–18. doi:10.1109/TIT.2005.860430. S2CID 14813938.
3. Welch, L. R. (1974). "Lower bounds on the maximum cross-correlation of signals". IEEE Transactions on Information Theory. 20 (3): 397–399. doi:10.1109/tit.1974.1055219.
4. Zhiqiang, Xu (April 2011). "Deterministic Sampling of Sparse Trigonometric Polynomials". Journal of Complexity. 27 (2): 133–140. arXiv:1006.2221. doi:10.1016/j.jco.2011.01.007. S2CID 2613562.
5. Donoho, D.L.; Michael Elad (March 2003). "Optimally sparse representation in general (nonorthogonal) dictionaries via L1 minimization". Proc. Natl. Acad. Sci. 100 (5): 2197–2202. Bibcode:2003PNAS..100.2197D. doi:10.1073/pnas.0437847100. PMC 153464. PMID 16576749.
6. Donoho, D.L.; Xiaoming Huo (November 2001). "Uncertainty principles and ideal atomic decomposition". IEEE Transactions on Information Theory. 47 (7): 2845–2862. CiteSeerX 10.1.1.39.3696. doi:10.1109/18.959265. S2CID 9500527.
7. Fuchs, J.-J. (June 2004). "On sparse representations in arbitrary redundant bases". IEEE Transactions on Information Theory. 50 (6): 1341–1344. doi:10.1109/TIT.2004.828141. S2CID 18432970.
8. Joel A. Tropp (2004). "Greed is good: Algorithmic results for sparse approximation" (PDF). CiteSeerX 10.1.1.84.5256.

Further reading

• Mutual coherence
• R1magic : R package providing mutual coherence computation.