Dictionary for matching pursuit
MPDICT = wmpdictionary(N)
[MPDICT,NBVECT]
= wmpdictionary(N)
[MPDICT,NBVECT]=
wmpdictionary(N,Name,Value)
[MPDICT,NBVECT,LST]
= wmpdictionary(N,Name,Value)
[MPDICT,NBVECT,LST,LONGS]
= wmpdictionary(N,Name,Value)
returns
the NbyP dictionary, MPDICT
= wmpdictionary(N
)MPDICT
, for the default
subdictionaries {{'sym4',5},{'wpsym4',5},'dct','sin'}
.
The column dimension of MPDICT
depends on N
.
[
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. See Visualize Haar Wavelet Dictionary for an example using LONGS
.

A positive integer equal to the length of your input signal.
The dictionary atoms are constructed to have 
Specify optional
commaseparated pairs of Name,Value
arguments. Name
is
the argument name and Value
is the corresponding value.
Name
must appear inside quotes. You can specify several name and value
pair arguments in any order as Name1,Value1,...,NameN,ValueN
.

Prepended subdictionary. The prepended subdictionary is an NbyM
matrix where N is the length of the input signal. 

Appended subdictionary. The appended subdictionary is a NbyM
matrix where N is the length of the input signal. 

A cell array of cell arrays with valid subdictionaries. Each cell array describes one subdictionary. Valid subdictionaries are:
Default: 

Matching pursuit dictionary. 

Number of vectors in subdictionaries. 

Cell array describing the dictionary. 

Cell array containing the number of elements for each subdictionary. 
[1] Cai, T.T. and L. Wang “Orthogonal Matching Pursuit for Sparse Signal Recovery with Noise”. IEEE^{®} Transactions on Information Theory, vol. 57, 7, 4680–4688, 2011.
[2] Donoho, D., M. Elad, and V. Temlyakov “Stable Recovery of Sparse Overcomplete Representations in the Presence of Noise”. IEEE Transactions on Information Theory, 52,1, 6–18, 2004.
[3] Mallat, S. and Z. Zhang “Matching Pursuits with TimeFrequency Dictionaries”. IEEE Transactions on Signal Processing, vol. 41, 12, 3397–3415, 1993
[4] Tropp, J.A. “Greed is good: Algorithmic results for sparse approximation”. IEEE Transactions on Information Theory, 50, pp. 2231–2242, 2004.