csci5521/assignments/hwk02/Back_Project.m
2023-10-25 05:20:44 -05:00

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939 B
Matlab

% implements the Back Projection algorithm, returns nothing but displays
% the first 5 eigenfaces of the training data after being 'back-projected'
% using the first 10, 50, and 100 eigenvectors of the data.
function [] = Back_Project(training_data, test_data, n_components)
% stack data
data = vertcat(training_data, test_data);
% perform PCA
[coeff, score] = pca(data);
% for each number of principal components
for n_idx = 1:length(n_components)
n = n_components(n_idx);
% perform the back projection algorithm using the first n_components(n) principal components
W = coeff(:,1:n);
z = score(:,1:n);
sample_mean = mean(data);
reconstruction = W * z' + sample_mean';
% plot first 5 images back projected using the first
% n_components(n) principal components
for i = 1:5
subplot(3,2,i)
imagesc(reshape(reconstruction(:,i),32,30)');
end
end
end % Function end