Higher-order Nonnegative CANDECOMP/PARAFAC Tensor Decomposition Using Proximal Algorithm
Deqing Wang, Fengyu Cong, Tapani Ristaniemi
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 higher-order (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.
BibTeX
@inproceedings{icassp2019_higherordernonne,
title = {Higher-order Nonnegative CANDECOMP/PARAFAC Tensor Decomposition Using Proximal Algorithm},
author = {Deqing Wang and Fengyu Cong and Tapani Ristaniemi},
booktitle = {ICASSP 2019},
year = {2019}
}