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  1. code/soft_rs/rks2017/RKS_classification.m +3 -42
  2. code/soft_rs/rks2017/RKS_regression.m +3 -32
  3. people/excathedra/AdaptVQ_ieeetgars_2012.pdf +3 -0
  4. people/excathedra/Camps-Valls08_JMLR.pdf +3 -0
  5. people/excathedra/Gomez-Perez05_IEEETNN.pdf +3 -0
  6. people/excathedra/Gutmann_PLOS_ONE_2014.pdf +3 -0
  7. people/excathedra/IJNS_Laparra14_accepted_v5.pdf +3 -0
  8. people/excathedra/LNAI10_malo_laparra.pdf +3 -0
  9. people/excathedra/Laparra11.pdf +3 -0
  10. people/excathedra/Laparra_JOSA_10.pdf +3 -0
  11. people/excathedra/Malo_Alheteia_08.pdf +3 -0
  12. people/excathedra/Malo_Laparra_Neural_10b.pdf +3 -0
  13. people/excathedra/Neco_accepted_2012.pdf +3 -0
  14. people/excathedra/Redundancy_Reduction_Malo_99.pdf +3 -0
  15. people/excathedra/SlidesNeuroImageMeeting11.pdf +3 -0
  16. people/excathedra/TR-532.pdf +3 -0
  17. people/excathedra/Tesis_JuanGutierrez.zip +3 -0
  18. people/excathedra/The_Manifest.pdf +3 -0
  19. people/excathedra/Thesis_Valero_Laparra.pdf +3 -0
  20. people/excathedra/V1_from_non_linear_ICA.pdf +3 -0
  21. people/excathedra/displays_99.pdf +3 -0
  22. people/excathedra/drr_jstsp2014_final.pdf +3 -0
  23. people/excathedra/icip02.pdf +3 -0
  24. people/excathedra/ieeeoct01.pdf +3 -0
  25. people/excathedra/josa_04.pdf +3 -0
  26. people/excathedra/laparra10a.pdf +3 -0
  27. people/excathedra/malo15a-reprint.pdf +3 -0
  28. people/excathedra/manuscr_TGRS_2012_00431.pdf +3 -0
  29. people/excathedra/manuscript4.pdf +3 -0
  30. people/excathedra/manuscript_TIP_00864_2004_R2.pdf +3 -0
  31. people/excathedra/msc_jmalo.pdf +3 -0
  32. people/excathedra/patente_v5_jesus.pdf +3 -0
  33. people/excathedra/patt_rec03.pdf +3 -0
  34. people/excathedra/proyecto_docente_ch3_2002.pdf +3 -0
  35. people/excathedra/rem_sens_im_proc_12_ch02.pdf +3 -0
  36. people/excathedra/tesis_irene.pdf +3 -0
  37. people/gustau/cv_gustau_camps_2023.pdf +3 -0
code/soft_rs/rks2017/RKS_classification.m CHANGED
@@ -1,42 +1,3 @@
1
- % Random Kitchen Sinks (RKS) for Least squares kernel classification
2
- %
3
- % We are given the following training-test data matrices
4
- % Xtrain: N x d, Xtest: M x d
5
- % Ytrain: N x 1, Ytest: M x 1
6
-
7
- close all, clear, clc
8
-
9
- % synthetic sample data
10
- N = 1e4;M = 1e3;d = 2;
11
- Xtrain = [randn(N/2,d); 3+randn(N/2,d)];Ytrain = [ones(N/2,1); -ones(N/2,1)];
12
- Xtest = [randn(M/2,d); 3+randn(M/2,d)];Ytest = [ones(M/2,1); -ones(M/2,1)];
13
-
14
- % Training
15
- D = 100; % number of random features
16
- lambda = 1e-9; % regularization parameter
17
- sigma = 2; % kernel parameter
18
- W = (1/sigma)*randn(D,d); % Random projection matrix
19
- Z = exp(1i*Xtrain*W'); % Explicitly projected features, N x D
20
- alpha = (Z'*Z + lambda*eye(D) )\(Z'*Ytrain); % Primal weights, D x 1
21
-
22
- % Testing
23
- Ztest = exp(1i*Xtest*W'); % M x D
24
- Ypred = sign(real(Ztest*alpha)); % M x 1
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-
26
- % Overall Accuracy
27
- acc = sum(Ypred == Ytest)/length(Ypred)
28
-
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- % Plot
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- figure,scatter(Xtrain(1:N/2,1),Xtrain(1:N/2,2),'+'), hold on,
31
- scatter(Xtrain(N/2+1:end,1),Xtrain(N/2+1:end,2),'ro')
32
- x=(min(Xtrain(:,1)):.05:max(Xtrain(:,1)))';
33
- y=(min(Xtrain(:,2)):.05:max(Xtrain(:,2)))';
34
- for i=1:length(x)
35
- for j=1:length(y)
36
- if abs(real(exp(1i*[x(i) y(j)]*W')*alpha)) < 1e-1
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- plot(x(i),y(j),'k-')
38
- end
39
- end
40
- end
41
- xlabel('feature 1'), ylabel('feature 2')
42
- legend('class +1', 'class -1','decission boundary'),grid on
 
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code/soft_rs/rks2017/RKS_regression.m CHANGED
@@ -1,32 +1,3 @@
1
- % Random Kitchen Sinks (RKS) for Least squares kernel regression
2
- %
3
- % We are given the following training-test data matrices
4
- % Xtrain: N x d, Xtest: M x d
5
- % Ytrain: N x 1, Ytest: M x 1
6
-
7
- close all, clear, clc
8
-
9
- % synthetic sample data
10
- N = 1e4;M = 1e3;d = 1;
11
- Xtrain = randn(N,d);Ytrain = sin(Xtrain) + .1*randn(N,d);
12
- Xtest = randn(M,d);Ytest = sin(Xtest) + .1*randn(M,d);
13
-
14
- % Training
15
- D = 100; % number of random features
16
- lambda = 1e-3; % regularization parameter
17
- sigma = 1; % kernel parameter
18
- W = (1/sigma)*randn(D,d); % Random projection matrix
19
- Z = exp(1i*Xtrain*W'); % Explicitly projected features, N x D
20
- alpha = (Z'*Z + lambda*eye(D) )\(Z'*Ytrain); % Primal weights, D x 1
21
-
22
- % Testing
23
- Ztest = exp(1i*Xtest*W'); % M x D
24
- Ypred = real(Ztest*alpha); % M x 1
25
-
26
- % RMSE
27
- norm(Ypred - Ytest)/sqrt(length(Ypred))
28
-
29
- figure,plot(Xtrain,Ytrain,'.'), hold on,
30
- x=(min(Xtrain):.01:max(Xtrain))';
31
- plot(x,real(exp(1i*x*W')*alpha),'r-','linewidth',2)
32
- legend('training set', 'fitted curve'), grid on
 
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