# why we need to select the smallest singular values in locally linear embedding (LLE)?

I'm learning about locally linear embedding. The cost function for finding embedded data is given by

$\Phi(X) = \Sigma_{ij}M_{ij}(X_i.X_j^T$)
Why we need to select the $2^{nd}$ to $(P+1)^{th}$ lowest eigenvectors of $M$?