Multi-Aspect Streaming Tensor Ring Completion for Dynamic Incremental Data

Zhenhao Huang, Yuning Qiu, Jinshi Yu, Guoxu Zhou

Abstract: As the volume of real-world data with numerous missing entries continues to grow rapidly, tensor completion has been a powerful tool to enhance such flawed data analysis. While existing methods mainly consider static data, there is a great need to deal with streaming data. In this letter, a multi-aspect streaming tensor ring completion (MASTR) method is proposed, where the low-rank tensor ring (TR) model is exploited to capture subspace information and transfer high-order correlations between multiple sub-tensors. Experimental results on synthetic data, hyperspectral data and video data demonstrate superior recovery performance compared to state-of-the-art methods.