[NumPy-Tickets] [NumPy] #1510: Feature: higher order singular value decomposition (HOSVD)

[NumPy Trac]

#1510: Feature: higher order singular value decomposition (HOSVD)
--------------------------------------------------------------+-------------
 Reporter:  jiahao                                            |       Owner:  pv         
     Type:  enhancement                                       |      Status:  new        
 Priority:  normal                                            |   Milestone:  Unscheduled
Component:  numpy.linalg                                      |     Version:  devel      
 Keywords:  tensor, hosvd, svd, singular value decomposition  |  
--------------------------------------------------------------+-------------
 Hi there,

 I recently need to compute higher order singular value decompositions
 (HOSVDs) on some tensors. I coded up a quick and dirty implementation as
 described in the original paper and thought it might be a useful
 contribution to the numpy.linalg library. The code is attached and
 released to you under public domain.

 If you do choose to incorporate the code, please note that:

 1) the code is a direct implementation of the algorithm described in the
 original paper, and could probably be done more quickly and cleverly,
 2) it has only been tested on the few examples in the original paper
 3) there are some discrepancies up to overall signs in some columns of the
 left singular matrices.

 cite:
     Lieven de Lathauwer, Bart de Moor, Joos Vandewalle,
     'A multilinear singular value decomposition',
     SIAM J. Matrix Anal. Appl. 21 (4), 2000, 1253-1278

-- 
Ticket URL: <http://projects.scipy.org/numpy/ticket/1510>
NumPy <http://projects.scipy.org/numpy>
My example project