trapca (Neural Network Toolbox)

Principal component transformation

[Ptrans] = trapca(P,TransMat)

trapca preprocesses the network input training set by applying the principal component transformation that was previously computed by prepca. This function needs to be used when a network has been trained using data normalized by prepca. All subsequent inputs to the network need to be transformed using the same normalization.

trapca(P,TransMat) takes these inputs,

P - R x Q matrix of centered input (column) vectors.

TransMat - Transformation matrix.

and returns,

Ptrans - Transformed data set.

Examples

Here is the code to perform a principal component analysis and retain only those components which contribute more than 2 percent to the variance in the data set. prestd is called first to create zero mean data, which are needed for prepca.

p = [-1.5 -0.58 0.21 -0.96 -0.79; -2.2 -0.87 0.31 -1.4  -1.2];
t = [-0.08 3.4 -0.82 0.69 3.1];
[pn,meanp,stdp,tn,meant,stdt] = prestd(p,t);
[ptrans,transMat] = prepca(pn,0.02);
net = newff(minmax(ptrans),[5 1],{'tansig''purelin'},'trainlm');
net = train(net,ptrans,tn);

If we then receive new inputs to apply to the trained network, we will use trastd and trapca to transform them first. Then the transformed inputs can be used to simulate the previously trained network. The network output must also be unnormalized using poststd.

p2 = [1.5 -0.8;0.05 -0.3];
[p2n] = trastd(p2,meanp,stdp);
[p2trans] = trapca(p2n,TransMat)
an = sim(net,p2trans);
[a] = poststd(an,meant,stdt);

Algorithm

Ptrans = TransMat*P;