# wmpdictionary

(To be removed) Dictionary for matching pursuit

`wmpdictionary`

will be removed in a future release. Use `sensingDictionary`

instead. For more information, see Compatibility Considerations.

## Syntax

## Description

`[`

returns the row vector, `MPDICT`

,`NBVECT`

]
= wmpdictionary(`N`

)`NBVECT`

, which contains the number of
vectors in each subdictionary. The order of the elements in
`NBVECT`

corresponds to the order of the subdictionaries and
any prepended or appended subdictionaries. The sum of the elements in
`NBVECT`

is the column dimension of
`MPDICT`

.

`[`

returns the dictionary, `MPDICT`

,`NBVECT`

]=
wmpdictionary(`N`

,`Name,Value`

)`MPDICT`

, using additional options
specified by one or more `Name,Value`

pair arguments.

`[`

returns the cell array, `MPDICT`

,`NBVECT`

,`LST`

]
= wmpdictionary(`N`

,`Name,Value`

)`LST`

, with descriptions of the
subdictionaries.

`[`

returns the cell array, `MPDICT`

,`NBVECT`

,`LST`

,`LONGS`

]
= wmpdictionary(`N`

,`Name,Value`

)`LONGS`

, containing the number of vectors
in each subdictionary. `LONGS`

is only useful for wavelet
subdictionaries. In wavelet subdictionaries, the corresponding element in
`LONGS`

gives the number of scaling functions at the coarsest
level and wavelet functions by level.

## Examples

## Input Arguments

## Output Arguments

## More About

## References

[1] Cai, T. Tony, and Lie Wang.
“Orthogonal Matching Pursuit for Sparse Signal Recovery With Noise.” *IEEE
Transactions on Information Theory* 57, no. 7 (July 2011): 4680–88.
https://doi.org/10.1109/TIT.2011.2146090.

[2] Donoho, D.L., M. Elad, and
V.N. Temlyakov. “Stable Recovery of Sparse Overcomplete Representations in the Presence
of Noise.” *IEEE Transactions on Information Theory* 52, no. 1
(January 2006): 6–18. https://doi.org/10.1109/TIT.2005.860430.

[3] Mallat, S.G. and Zhifeng
Zhang. “Matching Pursuits with Time-Frequency Dictionaries.” *IEEE
Transactions on Signal Processing* 41, no. 12 (December 1993): 3397–3415.
https://doi.org/10.1109/78.258082.

[4] Tropp, J.A. “Greed Is Good:
Algorithmic Results for Sparse Approximation.” *IEEE Transactions on
Information Theory* 50, no. 10 (October 2004): 2231–42.
https://doi.org/10.1109/TIT.2004.834793.

## Version History

**Introduced in R2012a**