Desktop.rar_MDS matlab_MDS 降维_mds_改进 数据_高维数据

function t_point = out_of_sample(point, mapping) %TRANSFORM_SAMPLE_EST Performs out-of-sample extension of new datapoints % % t_points = out_of_sample(points, mapping) % % Performs out-of-sample extension of the new datapoints in points. The % information necessary for the out-of-sample extension is contained in % mapping (this struct can be obtained from COMPUTE_MAPPING). % The function returns the coordinates of the transformed points in t_points. % % % This file is part of the Matlab Toolbox for Dimensionality Reduction v0.7b. % The toolbox can be obtained from http://ticc.uvt.nl/~lvdrmaaten % You are free to use, change, or redistribute this code in any way you % want for non-commercial purposes. However, it is appreciated if you % maintain the name of the original author. % % (C) Laurens van der Maaten % Tilburg University, 2008 welcome; % Handle PRTools dataset if strcmp(class(point), 'dataset') prtools = 1; ppoint = point; point = point.data; else prtools = 0; end switch mapping.name % Linear mappings case {'PCA', 'LDA', 'LPP', 'NPE', 'LLTSA', 'SPCA', 'PPCA', 'FA'} t_point = (point - repmat(mapping.mean, [size(point, 1) 1])) * mapping.M; % Kernel PCA mapping case 'KernelPCA' % Compute and center kernel matrix K = gram(mapping.X, point, mapping.kernel, mapping.param1, mapping.param2); J = repmat(mapping.column_sums', [1 size(K, 2)]); K = K - repmat(sum(K, 1), [size(K, 1) 1]) - J + repmat(mapping.total_sum, [size(K, 1) size(K, 2)]); % Compute transformed points t_point = mapping.invsqrtL * mapping.V' * K; t_point = t_point'; % Neural network mapping (with biases) case 'AutoEncoderEA' % Make zero-mean, unit variance point = point - repmat(mapping.mean, [size(point, 1) 1]); point = point ./ repmat(mapping.var, [size(point, 1) 1]); % Run through autoencoder point = [point ones(size(point, 1), 1)]; w1probs = [1 ./ (1 + exp(-point * mapping.w1)) ones(size(point, 1), 1)]; w2probs = [1 ./ (1 + exp(-w1probs * mapping.w2)) ones(size(point, 1), 1)]; w3probs = [1 ./ (1 + exp(-w2probs * mapping.w3)) ones(size(point, 1), 1)]; t_point = w3probs * mapping.w4; case 'Autoencoder' point = point - mapping.min; point = point ./ mapping.max; t_point = run_data_through_autoenc(mapping.network, point); case {'Isomap', 'LandmarkIsomap'} % Precomputations for speed if strcmp(mapping.name, 'Isomap') invVal = inv(diag(mapping.val)); [val, index] = sort(mapping.val, 'descend'); mapping.landmarks = 1:size(mapping.X, 1); else val = mapping.beta .^ (1 / 2); [val, index] = sort(real(diag(val)), 'descend'); end val = val(1:mapping.no_dims); meanD1 = mean(mapping.DD .^ 2, 1); meanD2 = mean(mean(mapping.DD .^ 2)); % Process all points (notice that in this implementation % out-of-sample points are not used as landmark points) points = point; t_point = repmat(0, [size(point, 1) mapping.no_dims]); for i=1:size(points, 1) % Compute distance of new sample to training points point = points(i,:); tD = L2_distance(point', mapping.X'); [tmp, ind] = sort(tD); tD(ind(mapping.k + 2:end)) = 0; tD = sparse(tD); tD = dijkstra([0 tD; tD' mapping.D], 1); tD = tD(mapping.landmarks + 1) .^ 2; % Compute point embedding subB = -.5 * (tD - repmat(mean(tD, 2), [1 size(tD, 2)]) - meanD1 - meanD2); if strcmp(mapping.name, 'LandmarkIsomap') vec = subB * mapping.alpha * mapping.invVal; vec = vec(:,index(1:mapping.no_dims)); else vec = subB * mapping.vec * invVal; vec = vec(:,index(1:mapping.no_dims)); end t_point(i,:) = real(vec .* sqrt(val)'); end case 'LLE' % Initialize some variables n = size(mapping.X, 1); t_point = repmat(0, [size(point, 1) numel(mapping.val)]); % Compute local Gram matrix D = (L2_distance(point', mapping.X') .^ 2); [foo, ind] = sort(D, 2, 'ascend'); for i=1:size(point, 1) % Compute local Gram matrix C = (repmat(point(i,:), [mapping.k 1]) - mapping.X(ind(i, 2:mapping.k + 1),:)) * ... (repmat(point(i,:), [mapping.k 1]) - mapping.X(ind(i, 2:mapping.k + 1),:))'; % Compute reconstruction weights invC = inv(C); W = sum(invC, 2) ./ sum(sum(invC)); % Compute kernel matrix K = repmat(0, [n 1]); K(ind(i, 2:mapping.k + 1)) = W; % Compute embedded point t_point(i,:) = sum(mapping.vec .* repmat(K, [1 size(mapping.vec, 2)]), 1); end case 'Laplacian' % Initialize some other variables n = size(mapping.X, 1); % Compute embeddings t_point = repmat(0, [size(point, 1) numel(mapping.val)]); for i=1:size(point, 1) % Compute Gaussian kernel between test point and training points K = (L2_distance(point(i,:)', mapping.X') .^ 2)'; [foo, ind] = sort(K, 'ascend'); K(ind(mapping.k+1:end)) = 0; K(K ~= 0) = exp(-K(K ~= 0) / (2 * mapping.sigma ^ 2)); % Normalize kernel K = (1 ./ n) .* (K ./ sqrt(mean(K) .* mean(mapping.K, 2))); % Compute embedded point t_point(i,:) = sum(mapping.vec .* repmat(K, [1 size(mapping.vec, 2)]), 1); end case 'LandmarkMVU' % Initialize some variables n = size(point, 1); % Compute pairwise distances X = L2_distance(point', mapping.X); neighbors = zeros(mapping.k2, n); % Compute reconstruction weights tol = 1e-7; Pia = sparse([], [], [], mapping.no_landmarks, n); for i=1:n % Identify nearest neighbors in distance matrix dist = L2_distance(X(:,i), mapping.D); [dist, ind] = sort(dist, 'ascend'); neighbors(:,i) = ind(2:mapping.k2 + 1); % Compute reconstruction weights z = mapping.D(:,neighbors(:,i)) - repmat(X(:,i), 1, mapping.k2); C = z' * z; C = C + tol * trace(C) * eye(mapping.k2) / mapping.k2; invC = inv(C); Pia(neighbors(:,i), i) = sum(invC)' / sum(sum(invC)); end % Fill sparse LLE weight matrix M = speye(n) + sparse([], [], [], n, n, n * mapping.k2 .^ 2); for i=1:n j = neighbors(:,i); w = Pia(j, i); M(i, j) = M(i, j) - w'; M(j, i) = M(j, i) - w; M(j, j) = M(j, j) + w * w'; end % Invert LLE weight matrix Pia = -M(mapping.no_landmarks + 1:end, mapping.no_landmarks + 1:end) \ ... M(mapping.n