K-SVD can be regarded as a generalized form of K-means. The total K-means algorithm can only approximate one signal for each semaphore, and each signal in K-SVD is a linear combination of multiple atoms To express. K-SVD through the construction of the dictionary to sparse data representation, often used for image compression, encoding, classification and other applications.

Filename	Size	Update
images\barbara.png	185727	2001-07-12
images\boats.png	177762	2001-07-12
images\house.png	34985	2005-09-11
images\lena.png	151199	2009-10-04
images\peppers256.png	40181	2002-08-29
images\PWCImage.png	8601	2011-11-01
AddOneElement.m	2304	2012-10-28
AnalysisKSVD.m	6301	2013-10-16
col2imstep.c	3924	2011-05-16
col2imstep.m	1001	2011-05-16
col2imstep.mexw32	7680	2011-05-16
ComputeOrthoSet.m	1063	2012-10-28
Contents.m	3115	2012-10-28
countcover.m	1178	2011-05-16
dictdist.m	2117	2012-10-27
DisplayOmega.m	1268	2012-10-28
ErrorBGP.m	2064	2012-10-28
GenerateAnalysisSignals.m	2243	2012-10-28
GenerateOmegaDIF.m	1346	2012-10-28
GenerateRandomRow.m	966	2012-10-28
im2colstep.c	3388	2011-05-16
im2colstep.m	1341	2011-05-16
im2colstep.mexw32	7168	2011-05-16
multcols.m	79	2013-10-16
multrows.m	78	2013-10-16
normcols.m	371	2010-07-08
normrows.m	365	2010-07-08
RankBGP.m	1898	2012-10-28
readme.txt	703	2012-10-28
TestKSVDNaturalImages.m	3630	2013-10-16
TestKSVDOmegaDIF.m	2283	2012-10-28
TestKSVDPWCImage.m	3419	2013-10-16
TestKSVDRandomOmega.m	1645	2012-10-28
TestPursuitOmegaDIF.m	4070	2013-10-16
images	0	2013-10-16

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