% An experiment on the eigenface recognition% You may separate training and classification processes. %% Input%   (cell)   Xt   c cell of D x Ni matrix which contains a training data%                 where c is the number of classes and D is the number %                 of dimensions of the feature vector and Ni is the number%                 of samples (feature vectors) for class i, 1 <= i < = c.%   (cell)   Xq   c cell of D x Nj matrix which contains a query/test data%                 where the total number of vectors is N. %   (scalar) [M]  The number of feature dimension reduced. %                 When this is a vector, an experiment to see effects%                 of the number of reduced dimension is performed. % Output%   (vector) Classified 1 x N%   (scalar) Rate       1%   (vector) Rank       c x 1function [Classified, Rate, Rank] = Eigenface(Xt, Xq, M)%% Load[Xt Ct] = cvuCell2Mat(Xt);[Xq Cq] = cvuCell2Mat(Xq);[D, Nt] = size(Xt);if ~exist('M', 'var') || isempty(M),     M = min(D, Nt-1);end%% Training[U, Me, Lambda] = cvPca(Xt, M(end));%% Classificationfor m = length(M):-1:1    U = U(:,1:M(m));    Yt = cvPcaProj(Xt, U, Me);    Yq = cvPcaProj(Xq, U, Me);    [Classified{m}, Rank{m}] = cvKnn(Yq, Yt, Ct, 1);    Rate{m} = sum(Classified{m} == Cq) / size(Cq,2);end%% Plotif isscalar(M)    Classified = Classified{1};    Rank = Rank{1};    Rate = Rate{1};else    plot(M, Rate);    xlabel('Feature Dimension');    ylabel('Recognition Rate');    axis([M(1) M(end) 0 1.0]);end