10 citations · 13 across the 4 of their papers we have counts for
4 papers
An Efficient Tensor Completion Method via New Latent Nuclear Norm
Jinshi Yu, Weijun Sun, Yuning Qiu +1
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…
Tensor-Ring Nuclear Norm Minimization and Application for Visual Data Completion
Jinshi Yu, Chao Li, Qibin Zhao +1
Tensor ring (TR) decomposition has been successfully used to obtain the state-of-the-art performance in the visual data completion problem. However, the existing TR-based completio…
Low-Rank Embedding of Kernels in Convolutional Neural Networks under Random Shuffling
Chao Li, Zhun Sun, Jinshi Yu +2
Although the convolutional neural networks (CNNs) have become popular for various image processing and computer vision task recently, it remains a challenging problem to reduce the…
Learning the Hierarchical Parts of Objects by Deep Non-Smooth Nonnegative Matrix Factorization
Jinshi Yu, Guoxu Zhou, Andrzej Cichocki +1
Nonsmooth Nonnegative Matrix Factorization (nsNMF) is capable of producing more localized, less overlapped feature representations than other variants of NMF while keeping satisfac…