学术活动

Low-Rank and Sparse Enhanced Tucker Decomposition for Tensor Completion

2020-11-19 11:31

报告人: 凌晨

报告人单位: 杭州电子科技大学

时间: 2020.11.20 15:20

地点: 腾讯会议375 132 357

开始时间:

报告人简介: 教授

年:

日月:

Tensor completion refers to the task of estimating the missing data from an incomplete measurement or observation, which is a core problem frequently arising from the areas of big data analysis, computer vision, and network engineering. Due to the multidimensional nature of high-order tensors, the matrix approaches, e.g., matrix factorization and direct matricization of tensors, are often not ideal for tensor completion and recovery. Exploiting the potential periodicity and inherent correlation properties appeared in real-world tensor data, in this talk, we shall incorporate the low-rank and sparse regularization technique to enhance Tucker decomposition for tensor completion. A series of computational experiments on real-world datasets, including internet traffic data, color images, and face recognition, show that our model performs better than many existing state-of-the-art matricization and tensorization approaches in terms of achieving higher recovery accuracy.


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