An iterative deflation algorithm for exact CP tensor decomposition

The Canonical Polyadic (CP) tensor decomposition has become an attractive mathematical tool these last ten years in various fields. Yet, efficient algorithms are still lacking to compute the full CP decomposition, whereas rank-one approximations are rather easy to compute. We propose a new deflation-based iterative algorithm allowing to compute the full CP decomposition, by resorting only to rank-one approximations. An analysis of convergence issues is included, as well as computer experiments. Our theoretical and experimental results show that the algorithm converges almost surely.

[1]  P. Paatero A weighted non-negative least squares algorithm for three-way ‘PARAFAC’ factor analysis , 1997 .

[2]  Rasmus Bro,et al.  Multi-way Analysis with Applications in the Chemical Sciences , 2004 .

[3]  Na Li,et al.  Some Convergence Results on the Regularized Alternating Least-Squares Method for Tensor Decomposition , 2011, 1109.3831.

[4]  G. Golub,et al.  Tracking a few extreme singular values and vectors in signal processing , 1990, Proc. IEEE.

[5]  Andrzej Cichocki,et al.  Deflation method for CANDECOMP/PARAFAC tensor decomposition , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[6]  Joos Vandewalle,et al.  On the Best Rank-1 and Rank-(R1 , R2, ... , RN) Approximation of Higher-Order Tensors , 2000, SIAM J. Matrix Anal. Appl..

[7]  André Uschmajew,et al.  Local Convergence of the Alternating Least Squares Algorithm for Canonical Tensor Approximation , 2012, SIAM J. Matrix Anal. Appl..

[8]  Pierre Comon,et al.  Tensors : A brief introduction , 2014, IEEE Signal Processing Magazine.

[9]  Gene H. Golub,et al.  Genericity And Rank Deficiency Of High Order Symmetric Tensors , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[10]  P. Comon,et al.  Tensor decompositions, alternating least squares and other tales , 2009 .

[11]  Andrzej Cichocki,et al.  Fast Local Algorithms for Large Scale Nonnegative Matrix and Tensor Factorizations , 2009, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[12]  P. Comon,et al.  Subtracting a best rank-1 approximation does not necessarily decrease tensor rank , 2015 .

[13]  Pierre Comon,et al.  Subtracting a best rank-1 approximation may increase tensor rank , 2009, 2009 17th European Signal Processing Conference.

[14]  Berkant Savas,et al.  Algorithms in data mining using matrix and tensor methods , 2008 .

[15]  H. Kiers Towards a standardized notation and terminology in multiway analysis , 2000 .

[16]  Liqi Wang,et al.  On the Global Convergence of the Alternating Least Squares Method for Rank-One Approximation to Generic Tensors , 2014, SIAM J. Matrix Anal. Appl..

[17]  André Lima Férrer de Almeida,et al.  PARAFAC-based unified tensor modeling for wireless communication systems with application to blind multiuser equalization , 2007, Signal Process..

[18]  Pierre Comon,et al.  Handbook of Blind Source Separation: Independent Component Analysis and Applications , 2010 .

[19]  L. Lathauwer,et al.  On the Best Rank-1 and Rank-( , 2004 .