Zobrazeno 1 - 10
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pro vyhledávání: '"ZHANG, Hongbing"'
Autor:
Zhang, Hongbing
Low-rank tensor completion (LRTC) aims to recover a complete low-rank tensor from incomplete observed tensor, attracting extensive attention in various practical applications such as image processing and computer vision. However, current methods ofte
Externí odkaz:
http://arxiv.org/abs/2309.16208
Hybrid-driven MRF seismic inversion for gas sand identification: A case study in the Yinggehai Basin
Publikováno v:
In Geoenergy Science and Engineering January 2025 244
Autor:
Ding, Yunfa, Deng, Anxia, Yu, Hao, Zhang, Hongbing, Qi, Tengfei, He, Jipei, He, Chenjun, Jie, Hou, Wang, Zihao, Wu, Liangpin
Publikováno v:
In Computers in Biology and Medicine January 2025 184
Non-convex relaxation methods have been widely used in tensor recovery problems, and compared with convex relaxation methods, can achieve better recovery results. In this paper, a new non-convex function, Minimax Logarithmic Concave Penalty (MLCP) fu
Externí odkaz:
http://arxiv.org/abs/2206.13506
Publikováno v:
In Pattern Recognition December 2024 156
Autor:
Zhang, Yaozhong, Zhang, Hongbing, Yan, Changgen, Lan, Hengxing, Liu, Xin, Bao, Han, Zhang, He, Lei, Wenbin, Li, Sen, Ju, Pengfei, Dong, Zhonghong
Publikováno v:
In Measurement 15 February 2025 243
Low-rank tensor completion (LRTC) is an important problem in computer vision and machine learning. The minimax-concave penalty (MCP) function as a non-convex relaxation has achieved good results in the LRTC problem. To makes all the constant paramete
Externí odkaz:
http://arxiv.org/abs/2201.12709
Autor:
Zhang, Hongbing, Zhou, Yongxiao, Ai, Minjun, Tang, Zhiyong, Sun, Yue, Han, Xiuzhu, Hu, Shuxian, Li, Zhengkun, Zhou, Chang, Chen, Jun
Publikováno v:
In Ceramics International 15 August 2024 50(16):28275-28280
Autor:
Zhou, Julie Xia, Li, Linda Xiaoyan, Zhang, Hongbing, Agborbesong, Ewud, Harris, Peter C., Calvet, James P., Li, Xiaogang
Publikováno v:
In Kidney International August 2024 106(2):258-272
Tensor sparse modeling as a promising approach, in the whole of science and engineering has been a huge success. As is known to all, various data in practical application are often generated by multiple factors, so the use of tensors to represent the
Externí odkaz:
http://arxiv.org/abs/2109.12257