Multiple change point detection in tensors

TL;DR

This paper introduces a new criterion for detecting change points in tensor data, accommodating various tensor structures and providing consistent estimation of change points and their confidence intervals.

Contribution

It proposes a mode-based Frobenius distance and adaptive ratio statistics for change point detection in tensors, handling both dense and sparse structures.

Findings

Method effectively estimates change points in high-dimensional tensors.

Confidence intervals for change point locations are constructed.

Numerical studies demonstrate good finite sample performance.

Abstract

This paper proposes a criterion for detecting change structures in tensor data. To accommodate tensor structure with structural mode that is not suitable to be equally treated and summarized in a distance to measure the difference between any two adjacent tensors, we define a mode-based signal-screening Frobenius distance for the moving sums of slices of tensor data to handle both dense and sparse model structures of the tensors. As a general distance, it can also deal with the case without structural mode. Based on the distance, we then construct signal statistics using the ratios with adaptive-to-change ridge functions. The number of changes and their locations can then be consistently estimated in certain senses, and the confidence intervals of the locations of change points are constructed. The results hold when the size of the tensor and the number of change points diverge at certain rates, respectively. Numerical studies are conducted to examine the finite sample performances of the proposed method. We also analyze two real data examples for illustration.