(1) Input: data tensor , and dimensions of all -mode signal subspaces.
(2) Initialization   : For to , calculate the projectors given by HOSVD-( ):
 (a) -mode unfold into matrix ;
 (b) Compute the SVD of ;
 (c) Compute matrix formed by the eigenvectors associated with the largest singular values of
     . is the initial matrix of the -mode signal subspace orthogonal basis vectors;
 (d) Form the initial orthogonal projector on the -mode signal subspace;
 (e) Compute the HOSVD-( ) of tensor given by
   ;
(3) ALS loop: Repeat until convergence, that is, for example, while , being a prior
   fixed threshold,
 (a) For to :
  (i) Form : ;
  (ii) -mode unfold tensor into matrix ;
  (iii) Compute matrix ;
  (iv) Compute matrix composed of the eigenvectors associated with the largest eigenvalues
     of . is the matrix of the -mode signal subspace orthogonal basis vectors at the
     iteration;
  (v) Compute ;
 (b) Compute ;
 (c) Increment .
(4) Output: the estimated signal tensor is obtained through . is
   the rank- approximation of , where is the index of the last iteration after the
   convergence of TUCKALS3 algorithm.
Algorithm 1: Lower rank-( ) tensor approximation (LRTA-( ))