[94] | 1 | % script Arch1.m |
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| 2 | % Finds ICA representation of train and test images under Architecture I, |
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| 3 | % described in Bartlett & Sejnowski (1997, 1998), and Bartlett, Movellan & |
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| 4 | % Sejnowski (2002): In Architecture I, we load N principal component |
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| 5 | % eigenvectors into rows of x, and then run ICA on x. |
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| 6 | % |
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| 7 | % Put the aligned training images in the rows of C, one image per row. |
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| 8 | % In the following examples, there are 500 images of aligned faces of size |
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| 9 | % 60x60 pixels, so C is 500x3600. |
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| 10 | % |
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| 11 | % You can use the following matlab code to create C: |
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| 12 | % markFeatures.m collects eye and mouth positions. |
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| 13 | % align_Faces.m crops, aligns, and scales the face images. |
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| 14 | % loadFaceMat.m loads the images into the rows of C. |
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| 15 | % |
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| 16 | % This script also calls the matrix of PCA eigenvectors organized in |
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| 17 | % the columns of V (3600x499), created by [V,R,E] = pcabigFn(C'); |
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| 18 | % |
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| 19 | % The ICA representation will be in the rows of F (called B in Bartlett, |
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| 20 | % Movellan & Sejnowski, 2002): |
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| 21 | |
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| 22 | [V,R,E] = pcabigFn(C'); |
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| 23 | %D = zeroMn(C')'; % D is 500x3600 and D = C-ones(500,1)*mean(C); |
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| 24 | %R = D*V; % R is 500x499 and contains the PCA coefficients; |
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| 25 | |
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| 26 | % We choose to use the first 200 eigenvectors. |
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| 27 | % (If PCA generalizes better by dropping first few eigenvectors, ICA will too). |
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| 28 | |
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| 29 | %x = V(:,1:200)'; % x is 200x3600 |
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| 30 | x = V(:,1:3)'; % x is 200x3600 |
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| 31 | runica % calculates wz, w and uu. The matrix x gets |
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| 32 | % overwritten by a sphered version of x. |
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| 33 | %F = R(:,1:200) * inv(w*wz); % F is 500x200 and each row contains the |
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| 34 | F = R(:,1:3) * inv(w*wz); % F is 500x200 and each row contains the |
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| 35 | % ICA1 rep of an image |
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| 36 | |
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| 37 | % Representations of test images under architecture I: |
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| 38 | % Put original aligned test images in rows of Ctest. |
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| 39 | |
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| 40 | Dtest = zeroMn(Ctest')'; % For proper testing, subtract the mean of the |
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| 41 | % training images not the test images: |
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| 42 | % Dtest = Ctest-ones(500,1)*mean(C); |
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| 43 | Rtest = Dtest*V; |
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| 44 | %Ftest = Rtest(:,1:200) * inv(w*wz); |
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| 45 | Ftest = Rtest(:,1:3) * inv(w*wz); |
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| 46 | |
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| 47 | % Test nearest neighbor classification using cosine, not euclidean distance, |
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| 48 | % as similarity measure. |
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| 49 | % |
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| 50 | % First create label vectors. These are column vectors of integers. Lets |
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| 51 | % say our 500 training examples consisted of 500 different people. Then |
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| 52 | % trainClass = [1:500]'; |
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| 53 | % |
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| 54 | % We also need the correct class labels of the test examples if we want to |
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| 55 | % compute percent correct. Lets say the test examples were two images each |
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| 56 | % of the first 10 individuals. Then |
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| 57 | % testClass = [1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10]'; |
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| 58 | |
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| 59 | trainClass = [1:3]'; |
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| 60 | testClass = [1 2 3]'; |
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| 61 | |
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| 62 | %We now compute percent correct: |
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| 63 | train_ex = F'; |
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| 64 | test_ex = Ftest'; |
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| 65 | [pc,rankmat] = nnclassFn(train_ex,test_ex,trainClass,testClass); |
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| 66 | |
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| 67 | %pc is percent correct of first nearest neighbor. |
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| 68 | %rankmat gives the top 30 matches for each test image. |
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| 69 | |
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