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.
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.
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.