Multidimensional Data Processing With Bayesian Inference via Structural Block Decomposition

doi:10.1109/TCYB.2023.3234356

Multidimensional Data Processing With Bayesian Inference via Structural Block Decomposition

2024-05-01

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DOI：10.1109/TCYB.2023.3234356

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How to handle large multidimensional datasets, such as hyperspectral images and video information, efficiently and effectively plays a critical role in big-data processing. The characteristics of low-rank tensor decomposition in recent years demonstrate the essentials in describing the tensor rank, which often leads to promising approaches. However, most current tensor decomposition models consider the rank-1 component simply to be the vector outer product, which may not fully capture the correlated spatial information effectively for large-scale and high-order multidimensional datasets. In this article, we develop a new novel tensor decomposition model by extending it to the matrix outer product or called Bhattacharya-Mesner product, to form an effective dataset decomposition. The fundamental idea is to decompose tensors structurally in a compact manner as much as possible while retaining data spatial characteristics in a tractable way. By incorporating the framework of the Bayesian inference, a new tensor decomposition model on the subtle matrix unfolding outer product is established for both tensor completion and robust principal component analysis problems, including hyperspectral image completion and denoising, traffic data imputation, and video background subtraction. Numerical experiments on real-world datasets demonstrate the highly desirable effectiveness of the proposed approach.

Keywords:

Bayesian inference

matrix outer product (MOP)

tensor completion (TC)

tensor decomposition

tensor robust principal component analysis (TRPCA)

Multidimensional Data Processing With Bayesian Inference via Structural Block Decomposition

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