A rank-adaptive higher-order orthogonal iteration algorithm for truncated Tucker decomposition

no code implementations • 25 Oct 2021 • Chuanfu Xiao, Chao Yang

We propose a novel rank-adaptive higher-order orthogonal iteration (HOOI) algorithm to compute the truncated Tucker decomposition of higher-order tensors with a given error tolerance, and prove that the method is locally optimal and monotonically convergent.

a-Tucker: Input-Adaptive and Matricization-Free Tucker Decomposition for Dense Tensors on CPUs and GPUs

no code implementations • 20 Oct 2020 • Min Li, Chuanfu Xiao, Chao Yang

A mode-wise flexible Tucker decomposition algorithm is proposed to enable the switch of different solvers for the factor matrices and core tensor, and a machine-learning adaptive solver selector is applied to automatically cope with the variations of both the input data and the hardware.

Efficient Alternating Least Squares Algorithms for Low Multilinear Rank Approximation of Tensors

no code implementations • 6 Apr 2020 • Chuanfu Xiao, Chao Yang, Min Li

In this paper, we propose a new class of truncated HOSVD algorithms based on alternating least squares (ALS) for efficiently computing the low multilinear rank approximation of tensors.