i all I am learning PCA method by reading up Turk&Petland papers etc while trying out PCA on a set of greyscale images using python, and numpy I tried to create eigenvectors and facespace. i have facesarray--- an NXP numpy.ndarray that contains data of images N=numof images,P=pixels in an image avgarray --1XP array containing avg value for each pixel adjustedfaces=facesarray-avgarray adjustedmatrix=matrix(adjustedfaces) adjustedmatrix_trans=adjustedmatrix.transpose() covariancematrix =adjustedmatrix*adjustedmatrix_trans evalues,evect=eigh(covariancematrix) after sorting such that most significant eigenvectors are selected. evectmatrix is now my eigenvectors matrix here is a sample using 4X3 greyscale images evalues [ -1.85852801e-13 6.31143639e+02 3.31182765e+03 5.29077871e+03] evect [[ 0.5 -0.06727772 0.6496399 -0.56871936] [ 0.5 -0.77317718 -0.37697426 0.10043632] [ 0.5 0.27108233 0.31014514 0.76179023] [ 0.5 0.56937257 -0.58281078 -0.29350719]] evectmatrix (sorted according to largest evalue first) [[-0.56871936 0.6496399 -0.06727772 0.5 ] [ 0.10043632 -0.37697426 -0.77317718 0.5 ] [ 0.76179023 0.31014514 0.27108233 0.5 ] [-0.29350719 -0.58281078 0.56937257 0.5 ]] then i can create facespace by facespace=evectmat*adjustedfaces till now i 've been following the steps as mentioned in the PCA tutorial(by Lindsay smith & others) what i want to know is that in the above evectmatrix is each row ([-0.56871936 0.6496399 -0.06727772 0.5 ] etc) an eigenvector? or does a column in the above matrix represent an eigenvector? to put it differently, should i represent an eigenvector by evectmatrix[i] or by (get_column_i_of(evectmatrix)).transpose() if someone can make this clear please do D