no code implementations • 20 Nov 2017 • Yuning Qiu, Guoxu Zhou, Kan Xie
Nonnegative Matrix Factorization (NMF) is a widely used technique for data representation.
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.
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 • 15 Mar 2022 • Yuning Qiu, Teruhisa Misu, Carlos Busso
The experimental results reveal that recordings annotated with events that are likely to be anomalous, such as avoiding on-road pedestrians and traffic rule violations, have higher anomaly scores than recordings without any event annotation.
no code implementations • 14 Mar 2022 • Yuning Qiu, Guoxu Zhou, Qibin Zhao, Shengli Xie
Experimental results on both synthetic and real-world data demonstrate the effectiveness and efficiency of the proposed model in recovering noisy incomplete tensor data compared with state-of-the-art tensor completion models.
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.
1 code implementation • 19 Oct 2021 • Tenghui Li, Guoxu Zhou, Yuning Qiu, Qibin Zhao
We make an attempt to understanding convolutional neural network by exploring the relationship between (deep) convolutional neural networks and Volterra convolutions.