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UOPC/Cone_Algo_Compare_Motion.m
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function [SSCO0, SSCO002, SSCO02,SSCO2,SSCO20,SSCO200,... | |
NCL0, NCL02, NCL002, NCL2, NCL20, NCL200, ... | |
KNNG2, KNNG4, KNNG6, KNNG8, KNNG10, KNNG12, KNNG14, KNNG16,... | |
KNN_M2, KNN_M4, KNN_M6, KNN_M8, KNN_M10, KNN_M12, KNN_M14, KNN_M16,... | |
TSC_Algo5, TSC_Algo10, TSC_Algo15, TSC_Algo20] = Cone_Algo_Compare(Data, Label) | |
%% | |
% addpath /Users/wenqiwang/Documents/cvx; cvx_startup; cvx_setup; | |
% Data given is unormalized data; | |
% SSC and NCL applies on unormalized data while KNN-SC appies on | |
% normalized data. | |
addpath SSC_ADMM_v1.1 | |
%% 0: SSC Algorithm (Know the clusters before hand, embeded in the SSC function) | |
disp('Sparse Subspace Clustering 0'); | |
SSCO0.r = 0; SSCO0.affine = false; SSCO0.outlier = true; | |
SSCO0.rho = 1; SSCO0.alpha = 0.000001; | |
[SSCO0.missrate,~, SSCO0.grps, ~, SSCO0.time] = SSC(Data,SSCO0.r,SSCO0.affine,SSCO0.alpha,SSCO0.outlier,SSCO0.rho,Label); | |
% .02: SSC Algorithm (Know the clusters before hand, embeded in the SSC function) | |
disp('Sparse Subspace Clustering .02'); | |
SSCO002.r = 0; SSCO002.affine = false; SSCO002.outlier = true; | |
SSCO002.rho = 1; SSCO002.alpha = .02; | |
[SSCO002.missrate,~, SSCO002.grps, ~, SSCO002.time] = SSC(Data,SSCO002.r,SSCO002.affine,SSCO002.alpha,SSCO002.outlier,SSCO002.rho,Label); | |
% .2: SSC Algorithm (Know the clusters before hand, embeded in the SSC function) | |
disp('Sparse Subspace Clustering .2'); | |
SSCO02.r = 0; SSCO02.affine = false; SSCO02.outlier = true; | |
SSCO02.rho = 1; SSCO02.alpha = .2; | |
[SSCO02.missrate,~, SSCO02.grps, ~, SSCO02.time] = SSC(Data,SSCO02.r,SSCO02.affine,SSCO02.alpha,SSCO02.outlier,SSCO02.rho,Label); | |
% 2: SSC Algorithm (Know the clusters before hand, embeded in the SSC function) | |
disp('Sparse Subspace Clustering 2'); | |
SSCO2.r = 0; SSCO2.affine = false; SSCO2.outlier = true; | |
SSCO2.rho = 1; SSCO2.alpha = 2; | |
[SSCO2.missrate,~, SSCO2.grps, ~, SSCO2.time] = SSC(Data,SSCO2.r,SSCO2.affine,SSCO2.alpha,SSCO2.outlier,SSCO2.rho,Label); | |
% 20: SSC Algorithm (Know the clusters before hand, embeded in the SSC function) | |
disp('Sparse Subspace Clustering 20'); | |
SSCO20.r = 0; SSCO20.affine = false; SSCO20.outlier = true; | |
SSCO20.rho = 1; SSCO20.alpha = 20; | |
[SSCO20.missrate,~, SSCO20.grps, ~, SSCO20.time] = SSC(Data,SSCO20.r,SSCO20.affine,SSCO20.alpha,SSCO20.outlier,SSCO20.rho,Label); | |
% 200: SSC Algorithm (Know the clusters before hand, embeded in the SSC function) | |
disp('Sparse Subspace Clustering 200'); | |
SSCO200.r = 0; SSCO200.affine = false; SSCO200.outlier = true; | |
SSCO200.rho = 1; SSCO200.alpha = 200; | |
[SSCO200.missrate,~, SSCO200.grps, ~, SSCO200.time] = SSC(Data,SSCO200.r,SSCO200.affine,SSCO200.alpha,SSCO200.outlier,SSCO200.rho,Label); | |
%% KNN (Know the number of clusters before hand): normalized data to do polyhedral cone clustering | |
% Data = [randn(2,100), 4+randn(2, 100)]; Label = [ones(1, 100), 2 * ones(1, 100)]; | |
disp('2-nearest Neghbour'); | |
KNNG2.k = 2/2; KNNG2.tau = 1; | |
[KNNG2.missrate, KNNG2.grps, ~, KNNG2.time] = KNNG_UOPCA(normc(Data), KNNG2.k, KNNG2.tau, Label); | |
disp('4-nearest Neghbour'); | |
KNNG4.k = 4/2; KNNG4.tau = 1; | |
[KNNG4.missrate, KNNG4.grps, ~, KNNG4.time] = KNNG_UOPCA(normc(Data), KNNG4.k, KNNG4.tau, Label); | |
disp('6-nearest Neghbour'); | |
KNNG6.k = 6/2; KNNG6.tau = 1; | |
[KNNG6.missrate, KNNG6.grps, ~, KNNG6.time] = KNNG_UOPCA(normc(Data), KNNG6.k, KNNG6.tau, Label); | |
disp('8-nearest Neghbour'); | |
KNNG8.k = 8/2; KNNG8.tau = 1; | |
[KNNG8.missrate, KNNG8.grps, ~, KNNG8.time] = KNNG_UOPCA(normc(Data), KNNG8.k, KNNG8.tau, Label); | |
disp('10-nearest Neghbour'); | |
KNNG10.k = 10/2; KNNG10.tau = 1; | |
[KNNG10.missrate, KNNG10.grps, ~, KNNG10.time] = KNNG_UOPCA(normc(Data), KNNG10.k, KNNG10.tau, Label); | |
disp('12-nearest Neghbour'); | |
KNNG12.k = 12/2; KNNG12.tau = 1; | |
[KNNG12.missrate, KNNG12.grps, ~, KNNG12.time] = KNNG_UOPCA(normc(Data), KNNG12.k, KNNG12.tau, Label); | |
disp('14-nearest Neghbour'); | |
KNNG14.k = 14/2; KNNG14.tau = 1; | |
[KNNG14.missrate, KNNG14.grps, ~, KNNG14.time] = KNNG_UOPCA(normc(Data), KNNG14.k, KNNG14.tau, Label); | |
disp('16-nearest Neghbour'); | |
KNNG16.k = 16/2; KNNG16.tau = 1; | |
[KNNG16.missrate, KNNG16.grps, ~, KNNG16.time] = KNNG_UOPCA(normc(Data), KNNG16.k, KNNG16.tau, Label); | |
%% | |
% Mutual KNN-SC | |
disp('2-nearest Mutual Neghbour'); | |
KNN_M2.k = 2/2; KNN_M2.tau = 1; | |
[KNN_M2.missrate, KNN_M2.grps, ~, KNN_M2.time] = KNNG_M(normc(Data), KNN_M2.k, KNN_M2.tau, Label); | |
disp('4-nearest Mutual Neghbour'); | |
KNN_M4.k = 4/2; KNN_M4.tau = 1; | |
[KNN_M4.missrate, KNN_M4.grps, ~, KNN_M4.time] = KNNG_M(normc(Data), KNN_M4.k, KNN_M4.tau, Label); | |
disp('6-nearest Mutual Neghbour'); | |
KNN_M6.k = 6/2; KNN_M6.tau = 1; | |
[KNN_M6.missrate, KNN_M6.grps, ~, KNN_M6.time] = KNNG_M(normc(Data), KNN_M6.k, KNN_M6.tau, Label); | |
disp('8-nearest Mutual Neghbour'); | |
KNN_M8.k = 8/2; KNN_M8.tau = 1; | |
[KNN_M8.missrate, KNN_M8.grps, ~, KNN_M8.time] = KNNG_M(normc(Data), KNN_M8.k, KNN_M8.tau, Label); | |
disp('10-nearest Mutual Neghbour'); | |
KNN_M10.k = 10/2; KNN_M10.tau = 1; | |
[KNN_M10.missrate, KNN_M10.grps, ~, KNN_M10.time] = KNNG_M(normc(Data), KNN_M10.k, KNN_M10.tau, Label); | |
disp('12-nearest Mutual Neghbour'); | |
KNN_M12.k = 12/2; KNN_M12.tau = 1; | |
[KNN_M12.missrate, KNN_M12.grps, ~, KNN_M12.time] = KNNG_M(normc(Data), KNN_M12.k, KNN_M12.tau, Label); | |
disp('14-nearest Mutual Neghbour'); | |
KNN_M14.k = 14/2; KNN_M14.tau = 1; | |
[KNN_M14.missrate, KNN_M14.grps, ~, KNN_M14.time] = KNNG_M(normc(Data), KNN_M14.k, KNN_M14.tau, Label); | |
disp('16-nearest Mutual Neghbour'); | |
KNN_M16.k = 16/2; KNN_M16.tau = 1; | |
[KNN_M16.missrate, KNN_M16.grps, ~, KNN_M16.time] = KNNG_M(normc(Data), KNN_M16.k, KNN_M16.tau, Label); | |
%% | |
% NCL (Know the number of clusters) | |
disp('NCL: lambda = 0'); | |
NCL0.lambda =0; | |
%[NCL0.missrate, NCL0.grps, ~, NCL0.time ] = NCL_UOPC( Data, NCL0.lambda, Label); | |
% NCL 0.02 | |
disp('NCL: lambda = 0.02'); | |
NCL002.lambda =0.02; | |
%[NCL002.missrate, NCL002.grps, ~, NCL002.time] = NCL_UOPC( Data, NCL002.lambda, Label); | |
% NCL 0.2 | |
disp('NCL: lambda = 0.2'); | |
NCL02.lambda =0.2; | |
%[NCL02.missrate, NCL02.grps, ~, NCL02.time] = NCL_UOPC( Data, NCL02.lambda, Label); | |
% NCL 2 | |
disp('NCL: lambda = 2'); | |
NCL2.lambda =2; | |
%[NCL2.missrate, NCL2.grps, ~, NCL2.time] = NCL_UOPC( Data, NCL2.lambda, Label); | |
% NCL 20 | |
disp('NCL: lambda = 20'); | |
NCL20.lambda =20; | |
%[NCL20.missrate, NCL20.grps, ~, NCL20.time] = NCL_UOPC( Data, NCL20.lambda, Label); | |
% NCL 200 | |
disp('NCL: lambda = 200'); | |
NCL200.lambda =200; | |
%[NCL200.missrate, NCL200.grps, ~, NCL200.time] = NCL_UOPC( Data, NCL200.lambda, Label); | |
%% | |
% TSC algorithm | |
disp('TSC algorithm 5'); | |
TSC_Algo5.q = 5; | |
[TSC_Algo5.missrate, TSC_Algo5.grps,~,TSC_Algo5.CPUtime] = TSC_Update(Data, TSC_Algo5.q,Label); | |
disp('TSC algorithm 10'); | |
TSC_Algo10.q = 10; | |
[TSC_Algo10.missrate, TSC_Algo10.grps,~,TSC_Algo10.CPUtime] = TSC_Update(Data, TSC_Algo10.q,Label); | |
disp('TSC algorithm 15'); | |
TSC_Algo15.q = 15; | |
[TSC_Algo15.missrate, TSC_Algo15.grps,~,TSC_Algo15.CPUtime] = TSC_Update(Data, TSC_Algo15.q,Label); | |
disp('TSC algorithm 20'); | |
TSC_Algo20.q = 20; | |
[TSC_Algo20.missrate, TSC_Algo20.grps,~, TSC_Algo20.CPUtime] = TSC_Update(Data, TSC_Algo20.q,Label); | |
end |