no code implementations • 14 Oct 2019 • Jinshi Yu, Weijun Sun, Yuning Qiu, Shengli Xie
In tensor completion, the latent nuclear norm is commonly used to induce low-rank structure, while substantially failing to capture the global information due to the utilization of unbalanced unfolding scheme.
1 code implementation • 2 Dec 2020 • Haonan Huang, Naiyao Liang, Wei Yan, Zuyuan Yang, Weijun Sun
To address these concerns, we present a partially shared semi-supervised deep matrix factorization model (PSDMF).
no code implementations • 6 Sep 2021 • Xinhai Zhao, Yuyuan Yu, Guoxu Zhou, Qibin Zhao, Weijun Sun
For the high dimensional data representation, nonnegative tensor ring (NTR) decomposition equipped with manifold learning has become a promising model to exploit the multi-dimensional structure and extract the feature from tensor data.
no code implementations • 27 Feb 2022 • Zhenhao Huang, Yuning Qiu, Xinqi Chen, Weijun Sun, Guoxu Zhou
Robust tensor completion (RTC) aims to recover a low-rank tensor from its incomplete observation with outlier corruption.
no code implementations • 4 Apr 2022 • Peilin Yang, Yonghui Huang, Yuning Qiu, Weijun Sun, Guoxu Zhou
The algorithm performs a composition of the completed tensor by initialising the factors from the FCTN decomposition.
no code implementations • 7 Oct 2022 • Peilin Yang, Weijun Sun, Qibin Zhao, Guoxu Zhou
The prevalent fully-connected tensor network (FCTN) has achieved excellent success to compress data.
no code implementations • 27 Nov 2022 • Yichun Qiu, Weijun Sun, Guoxu Zhou, Qibin Zhao
Efficient and accurate low-rank approximation (LRA) methods are of great significance for large-scale data analysis.
1 code implementation • 22 Oct 2023 • Junjia Huang, Haofeng Li, Weijun Sun, Xiang Wan, Guanbin Li
Automatic nuclei detection and classification can produce effective information for disease diagnosis.