Hello,
You can get the 2 basis vectors by simply sorting the eigen values in decreasing order and then usig the sorted indexes to index through the eigen vectors and get the vectors with the 2 largest eigen values as the basis vectors. The same is done in singular value decomposition done by "svd" function in MATLAB. Here is an example code:
A=randi(5,5);
B=cov(A)
[V,D]=eig(B)
[sorted_eigenvalues, idx] = sort(diag(D), 'descend')
sorted_eigenvectors = V(:, idx);
basis_vector_1 = sorted_eigenvectors(:, 1)
basis_vector_2 = sorted_eigenvectors(:, 2)
[U,S,V] = svd(B)
basis_vector_1 = U(:, 1)
basis_vector_2 = U(:, 2)
