Higher-order Nonnegative CANDECOMP/PARAFAC Tensor Decomposition Using Proximal Algorithm
Abstract
Tensor decomposition is a powerful tool for analyzing multiway data. Nowadays, with the fast development of multisensor technology, more and more data appear in higherorder (order > 4) and nonnegative form. However, the decomposition of higher-order nonnegative tensor suffers from
poor convergence and low speed. In this study, we propose a
new nonnegative CANDECOM/PARAFAC (NCP) model using proximal algorithm. The block principal pivoting method
in alternating nonnegative least squares (ANLS) framework
is employed to minimize the objective function. Our method
can guarantee the convergence and accelerate the computation. The results of experiments on both synthetic and real
data demonstrate the efficiency and superiority of our method.
Main Authors
Format
Conferences
Conference paper
Published
2019
Series
Subjects
Publication in research information system
Publisher
IEEE
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-201904232246Use this for linking
Parent publication ISBN
978-1-4799-8131-1
Review status
Peer reviewed
ISSN
1520-6149
DOI
https://doi.org/10.1109/ICASSP.2019.8683217
Conference
IEEE International Conference on Acoustics, Speech and Signal Processing
Language
English
Published in
Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
Is part of publication
ICASSP 2019 : Proceedings of the 2019 IEEE International Conference on Acoustics, Speech and Signal Processing
Citation
- Wang, D., Cong, F., & Ristaniemi, T. (2019). Higher-order Nonnegative CANDECOMP/PARAFAC Tensor Decomposition Using Proximal Algorithm. In ICASSP 2019 : Proceedings of the 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 3457-3461). IEEE. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing. https://doi.org/10.1109/ICASSP.2019.8683217
License
Copyright© 2019 IEEE.