I have read about similar questions. I have data which has 68 columns and about 800 samples. The 68. column is the output the rest 67 is the input variables. I want to reduce the size of my input variables to for example 30 or 20 variables.
I have read about PCA. I already ran the PCA in Matlab and gathered a 67 x 20 matrix containing PCA coefficients. I calculated eigenvalues for each Principal component (10 eigenvalues). As far as I understand I should order these eigenvalues and select the PCA's with higher eigenvalues as important. For example, I chose PC1, PC3, and PC9.
How can I use this information to select among the original 67 variables? I mean how can I use this PCA analysis results to reduce a 67*800 matrix to a 20*800 matrix and get the variables which have higher effect on the target variable?