clc；
clear all；
close all
load 1
Cst=10；
OptM=2
Lambda=0.001
Xp=data
CMat = SparseCoefRecovery(Xp,Cst,OptM,lambda);
[CMatC,sc,OutlierIndx,Fail] = OutlierDetection(CMat,s);
if (Fail == 0)
CKSym = BuildAdjacency(CMatC,K);
Grps = SpectralClustering(CKSym,n);
Grps = bestMap(sc,Grps);
Missrate = sum(sc(:) ~= Grps(:)) / length(sc);
save Lasso_001.mat CMat CKSym Missrate Fail
else
save Lasso_001.mat CMat Fail
end
2、谱聚类功能函数（CVX 功能包在附件中）
function [groups, kerNS] = SpectralClustering(CKSym,n)
warning off;
N = size(CKSym,1);
MAXiter = 1000; % Maximum number of iterations for KMeans
REPlic = 20; % Number of replications for KMeans
DN = diag( 1./sqrt(sum(CKSym)+eps) );
LapN = speye(N) - DN * CKSym * DN;
[~,~,vN] = svd(LapN);
