annotate DL/Majorization Minimization DL/README @ 160:e3035d45d014 danieleb

Added support classes
author Daniele Barchiesi <daniele.barchiesi@eecs.qmul.ac.uk>
date Wed, 31 Aug 2011 10:53:10 +0100
parents b14209313ba4
children
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ivan@155 1 Majorization Minimization Dictionary Learning (MMDL) Package (Beta version)
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ivan@155 3 This package includes the codes needed to generate some of the plots in the paper, "Dictionary Learning for Sparse Approximations with the Majorization Method" by M. Yaghoobi, T. Blumensath and M. Davies, IEEE Transaction on Signal Processing, Vol. 57, No. 6, pp 2178-2191, 2009.
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ivan@155 5 The package also includes the authors' implementations of some other well-known dictionary learning methods. These have been used for comparison reason.
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ivan@155 7 The package includes two parts:
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ivan@155 9 1- Building blocks of the MMDL:
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ivan@155 11 a) mm1 : Iterative Softthresholding for sparse approximation
ivan@155 12 b) rdlcn : MMDL with column norm constraint
ivan@155 13 c) rdlfn : MMDL with Frobenius norm constraint
ivan@155 14 d) softthreshold : Softthresholding operator
ivan@155 15 e) Demo : A demo to demonstrate how to use MMDL and recover a generic dictionary
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ivan@155 17 2- MMDL in action:
ivan@155 18 a) ExactDicoRecovery : Exact generic atom recovery experiment. It includes a comparison with other methods
ivan@155 19 b) Dictionary Learning for Audio : Dictionary learning with MMDL method applied to some classic music recorded from BBC radio 3
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ivan@155 21 Installation:
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ivan@155 23 You just need to decompress the package into your local disk and add the path of main directory to the Matlab path.
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ivan@155 25 Citation:
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ivan@155 27 This package is free to use. However, any publication based on using this package, in any kind, should cite the mentioned paper.
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ivan@155 29 Copyright:
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ivan@155 31 The use of this package does not need any farther permission, if it is restricted to the academic and industrial researches. Any military affiliated use of this package is strictly prohibited.