How to find Eigenvectors of high dimension CovarianceMatrix ?
I am implementing PCA function, so I can not just call PCA function of OpenCV. I have trouble in calculating eigenvectors using Eigen function, input covariance matrix is 10000x10000 (because image size is 100x100, so 10000 features for one image, training set is only 5 images), eigenvectors returned by Eigen is not usual, every row of eigenvectors has 9999 "0" and only one "1", 00000000.....0010000....00000, I think this vector is normalised already. I try to use low dimension input covariance matrix, eigenvectors is ok. I also try to use Eigen function of Eigen library(https://eigen.tuxfamily.org), but it take a lot of time and I can not wait. Any function from OpenCV I can use? Or I need to implement my own Eigen function to reduce cost?
