3/-LOCAL TANGENT SPACE ALIGNMEwith the keyword method = ’ltsa’ in phyton 
And for matlab we use function mappedX = ltsa(X, no_dims, k, eig_impl )\ref{div-121230}
http://scikit-learn.org/stable/modules/manifold.html#local-tangent-space-alignment
donc
 LTSA is a technique that describes local properties of the high-dimensional data using the local tangent space of each datapoint . LTSA is algorithmically similar to LLE can be put in this category. ""One advantage of LTSA over LLE is that using LTSA we can potentially detect the intrinsic dimension of the underlying manifold by analyzing the local tangent space structure\ref{contribution}"".
The LTSA algorithm comprises three stages:
  1.      Nearest Neighbors Search . Determine k nearest neighbors xij of xi, j = 1, . . . , k.
  2. Weight Matrix Construction( Constructing alingment matrix). Form the matrix B by locally summing (7.18) in the article if a direct eigen-solver will be used. Otherwise implement a routine that computes matrix-vector multiplication Bu for an arbitrary vector u.
  3. Aligning global coordinates Compute the d + 1 smallest eigenvectors of B and pick up the eigenvector matrix [u2, · · · , ud+1] corresponding to the 2nd to d+1st smallest eigenvalues, and set
    T = [u2, · · · , ud+1]T.