Multi-Aspect Streaming Tensor Ring Completion for Dynamic Incremental Data

Zhenhao Huang

doi:10.1109/LSP.2022.3231469

Multi-Aspect Streaming Tensor Ring Completion for Dynamic Incremental Data

2022-01-01

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DOI：10.1109/LSP.2022.3231469

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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.

Keywords:

Tensors

Streaming media

Data models

Computational modeling

Signal to noise ratio

Task analysis

Signal processing algorithms

Tensor completion

streaming data

tensor ring

sub-tensors

Multi-Aspect Streaming Tensor Ring Completion for Dynamic Incremental Data

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