Visual data completion method based on low-rank tensor ring decomposition and factor prior

Abstract

The invention discloses a visual data completion method based on low-rank tensor ring decomposition and factor prior, and aims to solve the problem that a traditional data completion algorithm based on tensor decomposition depends on initial rank selection, so that a recovery result lacks stability and effectiveness, and a layered tensor decomposition model is designed. Tensor ring decomposition and complementation are realized at the same time, and for the first layer, incomplete tensors are expressed as a series of third-order factors through tensor ring decomposition; for the second layer, the transformation tensor nuclear norm is used for representing the low-rank constraint of the factors, and the degree of freedom of each factor is limited in combination with the factor priori of graph regularization; according to the method, the low-rank structure and the prior information of the factor space are utilized at the same time, on one hand, the model has implicit rank adjustment, the robustness of the model to rank selection can be improved, and therefore the burden of searching the optimal initial rank is relieved, and on the other hand, potential information of tensor data is fully utilized, and the complementation performance is further improved.

Description

technical field [0001] The invention relates to the field of visual data completion, in particular to a visual data completion method based on low-rank tensor ring decomposition and factor prior. Background technique [0002] With the rapid development of information technology, modern society is entering an era of explosive growth of data, resulting in a large amount of multi-attribute and multi-related data. However, most of the data is usually incomplete, which may be due to occlusion, noise, local corruption, difficulty in collection, or data loss during transformation. Incomplete data can significantly reduce the quality of the data, making the analysis process difficult. As a high-dimensional extension of vectors and matrices, tensors can express more complex data internal structures and are widely used in signal processing, computer vision, data mining, and neuroscience. The matrix-based correlation completion method destroys the spatial structure characteristics of...

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Problems solved by technology

[0004] The present invention aims at the problem that the traditional data completion algorithm based on tensor decomposition relies on the initial rank selection, which leads to the lack of stability and effectiveness of the recovery results, and provides a method based on Visual Data Completion Approaches for Low-Rank Tensor Ring Decomposition and Factor Priors

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