ICA.rar_ica 特征_模式识别

function [patterns, targets, W, Aw, means] = ICA(patterns, targets, params) %Reshape the data points using the independent component analysis algorithm %Inputs: % train_patterns - Input patterns % train_targets - Input targets % params - [Output dimension, Learning rate] % %Outputs % patterns - New patterns % targets - New targets % W - Reshape martix % means - The means vector of the patterns [r,c] = size(patterns); [dimension, eta] = process_params(params); if (r < dimension), error('Output dimension cannot be larger than the input dimension') end %Whiten the data to zero mean and unit covariance means = mean(patterns')'; patterns = patterns - means*ones(1,c); [v, d] = eig(cov(patterns',1)); Aw = v*inv(sqrtm(d)); patterns = Aw'*patterns; %Move data to the range of [-1,1] minp = min(patterns')'; maxp = max(patterns')'; patterns = (patterns - minp*ones(1,c))./((maxp-minp)*ones(1,c)); patterns = patterns*2-1; %Find the weight matrix W = randn(r); iter = 1; while (iter < 1000), iter = iter + 1; y = W*patterns; phi = activation(y); dW = (eye(r) - 1/c*phi*y')*W; %Break if algorithm diverges if (max(max(dW)) > 1e3), disp(['Algorithm diverged after ' num2str(i) ' iterations']) break end W = W + eta*dW; update = max(max(abs(dW))); %If the algorithm converged, exit if (update < eta), disp(['Algorithm converged after ' num2str(iter) ' iterations']) break else if (iter / 10 == floor(iter/10)) disp(['Iteration ' num2str(iter) ': Maximum update is ' num2str(update)]) end end end %Take only the most influential outputs power = sum(abs(W)'); [m, in] = sort(power); W = W(in(r-dimension+1:r),:); %Calculate new patterns patterns = W*patterns; W = W*Aw; %End ICA function phi = activation(y) %Activation function for ICA phi = y.^3; %phi = tahn(y);