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Conference Paper (international conference)

Deflation Method for CANDECOMP/PARAFAC Tensor Decomposition

Phan A. H., Tichavský Petr, Cichocki A.

: 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), p. 6736-6740

: IEEE International Conference on Acoustics, Speech, and Signal Processing 2014 (ICASSP2014), (Florence, IT, 04.05.2014-09.05.2014)

: GA14-13713S, GA ČR

: tensor decomposition, PARAFAC, CANDECOMP

: 10.1109/ICASSP.2014.6854904

: http://library.utia.cas.cz/separaty/2014/SI/tichavsky-0427990.pdf

(eng): CANDECOMP/PARAFAC tensor decomposition (CPD) approximates multiway data by rank-1 tensors. Unlike matrix decomposition, the procedure which estimates the best rank-R tensor approximation through R sequential best rank-1 approximations does not work for tensors, because the deflation does not always reduce the tensor rank. In this paper we propose a novel deflation method for the problem in which rank R does not exceed the tensor dimensions. A rank-R CPD can be performed through (R−1) rank-1 reductions. At each deflation stage, the residue tensor is constrained to have a reduced multilinear rank.

: BB

2019-01-07 08:39