Robust Low-Rank Tensor Decomposition with the L2 Criterion

The growing prevalence of tensor data, or multiway arrays, in science and engineering applications motivates the need for tensor decompositions that are robust against outliers. In this article, we present a robust Tucker decomposition estimator based on the L-2 criterion, called the Tucker-L2E. Our numerical experiments demonstrate that Tucker-L2E has empirically stronger recovery performance in more challenging high-rank scenarios compared with existing alternatives. The appropriate Tucker-rank can be selected in a data-driven manner with cross-validation or hold-out validation. The practical effectiveness of Tucker-L2E is validated on real data applications in fMRI tensor denoising, PARAFAC analysis of fluorescence data, and feature extraction for classification of corrupted images.

Automated summary

In this article, a robust Tucker decomposition estimator called Tucker-L2E, based on the L-2 criterion, is presented to enhance the robustness against outliers. Numerical experiments demonstrate that Tucker-L2E has stronger recovery performance in challenging high-rank scenarios compared to existing alternatives. The appropriate Tucker-rank can be selected in a data-driven manner using cross-validation or hold-out validation. The practical effectiveness of Tucker-L2E is validated on real data applications in fMRI tensor denoising, PARAFAC analysis of fluorescence data, and feature extraction for classification of corrupted images.