www.pudn.com > RobustSF.zip > demo1.m, change:2014-09-01,size:1907b

% KSVD running file 
% in this file a synthetic test of the K-SVD algorithm is performed. First, 
% a random dictionary with normalized columns is being generated, and then 
% a set of data signals, each as a linear combination of 3 dictionary 
% element is created, with noise level of 20SNR. this set is given as input 
% to the K-SVD algorithm. 
% a different mode for activating the K-SVD algorithm is until a fixed 
% error is reached in the Sparse coding stage, instead until a fixed number of coefficients is found 
% (it was used by us for the 
% denoising experiments). in order to switch between those two modes just 
% change the param.errorFlag (0 - for fixed number of coefficients, 1 - 
% until a certain error is reached). 
param.L = 3;   % number of elements in each linear combination. 
param.K = 50; % number of dictionary elements 
param.numIteration = 50; % number of iteration to execute the K-SVD algorithm. 
param.errorFlag = 0; % decompose signals until a certain error is reached. do not use fix number of coefficients. 
%param.errorGoal = sigma; 
param.preserveDCAtom = 0; 
%%%%%%% creating the data to train on %%%%%%%% 
N = 1500; % number of signals to generate 
n = 20;   % dimension of each data 
SNRdB = 20; % level of noise to be added 
[param.TrueDictionary, D, x] = gererateSyntheticDictionaryAndData(N, param.L, n, param.K, SNRdB); 
%%%%%%%% initial dictionary: Dictionary elements %%%%%%%% 
param.InitializationMethod =  'DataElements'; 
param.displayProgress = 1; 
disp('Starting to  train the dictionary'); 
[Dictionary,output]  = KSVD(D,param); 
disp(['The KSVD algorithm retrived ',num2str(output.ratio(end)),' atoms from the original dictionary']); 
[Dictionary,output]  = MOD(D,param); 
disp(['The MOD algorithm retrived ',num2str(output.ratio(end)),' atoms from the original dictionary']);