An Iterative Reweighted Method for Tucker Decomposition of Incomplete Multiway Tensors

TL;DR

This paper introduces an iterative reweighted method for Tucker tensor decomposition that effectively handles incomplete multiway data, automatically determines model complexity, and improves computational efficiency.

Contribution

It proposes a novel group-based log-sum penalty functional and an iterative reweighted algorithm with an over-relaxed thresholding technique for low-rank tensor completion.

Findings

Competitive performance against existing algorithms

Automatic determination of multilinear rank

Reduced computational complexity

Abstract

We consider the problem of low-rank decomposition of incomplete multiway tensors. Since many real-world data lie on an intrinsically low dimensional subspace, tensor low-rank decomposition with missing entries has applications in many data analysis problems such as recommender systems and image inpainting. In this paper, we focus on Tucker decomposition which represents an Nth-order tensor in terms of N factor matrices and a core tensor via multilinear operations. To exploit the underlying multilinear low-rank structure in high-dimensional datasets, we propose a group-based log-sum penalty functional to place structural sparsity over the core tensor, which leads to a compact representation with smallest core tensor. The method for Tucker decomposition is developed by iteratively minimizing a surrogate function that majorizes the original objective function, which results in an iterative reweighted process. In addition, to reduce the computational complexity, an over-relaxed monotone fast iterative shrinkage-thresholding technique is adapted and embedded in the iterative reweighted process. The proposed method is able to determine the model complexity (i.e. multilinear rank) in an automatic way. Simulation results show that the proposed algorithm offers competitive performance compared with other existing algorithms.