Tucker tensor factor models: matricization and mode-wise PCA estimation

TL;DR

This paper introduces a mode-wise PCA approach for estimating Tucker tensor factor models, providing a new statistical inference framework and demonstrating its effectiveness in tensor reconstruction and clustering tasks.

Contribution

It develops a novel tensor estimation method by recasting tensor models into mode-wise PCA, with theoretical guarantees and practical applications.

Findings

Mode-wise PCA effectively estimates tensor signals.

Projection and iteration improve signal-to-noise ratio.

Method performs well in tensor reconstruction and clustering.

Abstract

High-dimensional, higher-order tensor data are gaining prominence in a variety of fields, including but not limited to computer vision and network analysis. Tensor factor models, induced from noisy versions of tensor decompositions or factorizations, are natural potent instruments to study a collection of tensor-variate objects that may be dependent or independent. However, it is still in the early stage of developing statistical inferential theories for the estimation of various low-rank structures, which are customary to play the role of signals of tensor factor models. In this paper, we attempt to ``decode" the estimation of a higher-order tensor factor model by leveraging tensor matricization. Specifically, we recast it into mode-wise traditional high-dimensional vector/fiber factor models, enabling the deployment of conventional principal components analysis (PCA) for estimation. Demonstrated by the Tucker tensor factor model (TuTFaM), which is induced from the noisy version of the widely-used Tucker decomposition, we summarize that estimations on signal components are essentially mode-wise PCA techniques, and the involvement of projection and iteration will enhance the signal-to-noise ratio to various extent. We establish the inferential theory of the proposed estimators, conduct rich simulation experiments, and illustrate how the proposed estimations can work in tensor reconstruction, and clustering for independent video and dependent economic datasets, respectively.