TY - GEN
T1 - A parallel algorithm for big tensor decomposition using randomly compressed cubes (PARACOMP)
AU - Sidiropoulos, Nikolaos
AU - Papalexakis, E. E.
AU - Faloutsos, C.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - A parallel algorithm for low-rank tensor decomposition that is especially well-suited for big tensors is proposed. The new algorithm is based on parallel processing of a set of randomly compressed, reduced-size 'replicas' of the big tensor. Each replica is independently decomposed, and the results are joined via a master linear equation per tensor mode. The approach enables massive parallelism with guaranteed identifiability properties: if the big tensor has low rank and the system parameters are appropriately chosen, then the rank-one factors of the big tensor will be exactly recovered from the analysis of the reduced-size replicas. The proposed algorithm is proven to yield memory / storage and complexity gains of order up to IJ/F for a big tensor of size I × J × K of rank F with F ≤I ≤J ≤K.
AB - A parallel algorithm for low-rank tensor decomposition that is especially well-suited for big tensors is proposed. The new algorithm is based on parallel processing of a set of randomly compressed, reduced-size 'replicas' of the big tensor. Each replica is independently decomposed, and the results are joined via a master linear equation per tensor mode. The approach enables massive parallelism with guaranteed identifiability properties: if the big tensor has low rank and the system parameters are appropriately chosen, then the rank-one factors of the big tensor will be exactly recovered from the analysis of the reduced-size replicas. The proposed algorithm is proven to yield memory / storage and complexity gains of order up to IJ/F for a big tensor of size I × J × K of rank F with F ≤I ≤J ≤K.
KW - Big Data
KW - CANDECOMP/PARAFAC
KW - Cloud Computing and Storage
KW - Parallel and Distributed Computation
KW - Tensor decomposition
UR - http://www.scopus.com/inward/record.url?scp=84905224559&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84905224559&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2014.6853546
DO - 10.1109/ICASSP.2014.6853546
M3 - Conference contribution
AN - SCOPUS:84905224559
SN - 9781479928927
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1
EP - 5
BT - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
Y2 - 4 May 2014 through 9 May 2014
ER -