I've been using SVM to classify a data set without applying PCA. The classification rate was not bad, but I thought maybe applying PCA increases performance.
I have a training set (without labels) with size of 700x60 (i.e. 700 feature vectors each comprises of 60 different features). I applied PCA to that matrix in Matlab, getting a 60x60 matrix.:
coeff = pca(trainVector);
But I am confused about what to do next. How should I utilize this information?
I applied PCA to only training set and have 60x60 PCA matrix only. What will be my new training set after PCA and what should I do with the test set? Something like transformation matrix? I wasn't doing something like cross validation. As I know, SVM algorithm already applies cross validation. Previously I used a SVM tool to get a prediction model and then use this Model and the SVM tool to classify test set. I wanted to improve the accuracy by using PCA before classification.