Hangjun Che

2.0k citations

79 papers · 1.4k · 2 hit papers · h-index 24

Impact in

Computational Mathematics top 1%
- Tensor decomposition and applications
Computer Vision and Pattern Recognition top 2%
- Face and Expression Recognition
- Image Retrieval and Classification Techniques

Papers in

Computer Vision and Pattern Recognition 44
- Face and Expression Recognition 29
- Image Retrieval and Classification Techniques 7
Artificial Intelligence 34
- Neural Networks and Applications 15

Co-authors: Jun Wang (12 shared papers)Man-Fai Leung (37 shared papers)Cheng Liu (11 shared papers)Chuandong Li (7 shared papers)Xing He (9 shared papers)Shiping Wen (5 shared papers)Tingwen Huang (5 shared papers)Chenglu Li (4 shared papers)
Journals: Neural Networks (11 papers)IEEE Transactions on Neural Networks and Learning Systems (7 papers)Information Fusion (4 papers)IEEE Transactions on Consumer Electronics (4 papers)IEEE Transactions on Computational Social Systems (3 papers)
Partner nations: China United Kingdom Hong Kong

In The Last Decade

Hangjun Che

70 papers receiving 1.4k citations

Hangjun Che's Hit Papers

align trajectories log scale

Projected cross-view learning for unbalanced incomplete multi-view clustering 2024 · 57 citations

Peers

Countries citing papers authored by Hangjun Che

Since Specialization

Citations

This map shows the geographic impact of Hangjun Che's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Hangjun Che with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hangjun Che more than expected).

Fields of papers citing papers by Hangjun Che

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hangjun Che. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Hangjun Che. The network helps show where Hangjun Che may publish in the future.

Co-authors

The 25 scholars most cited alongside Hangjun Che, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Hangjun Che Line = papers co-authored together Hangjun Che links everyone, so they are left out of the graph.

All Works

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20 of 20 papers shown

Showing the 20 most-cited of 79 papers — load more, or switch the sort, to bring in the rest.

#	Work
1	A collaborative neurodynamic approach to global and combinatorial optimization Neural Networks ·Hangjun Che, Jun Wang	2019	113
2	A Two-Timescale Duplex Neurodynamic Approach to Mixed-Integer Optimization IEEE Transactions on Neural Networks and Learning Systems ·Hangjun Che, Jun Wang	2020	87
3	Robust multi-view non-negative matrix factorization with adaptive graph and diversity constraints Information Sciences ·Chenglu Li, Hangjun Che, Man-Fai Leung, Cheng Liu, (unknown) Hit paper breakdown →	2023	85
4	A Two-Timescale Duplex Neurodynamic Approach to Biconvex Optimization IEEE Transactions on Neural Networks and Learning Systems ·Hangjun Che, Jun Wang	2018	69
5	Projected cross-view learning for unbalanced incomplete multi-view clustering Information Fusion ·Yiran Cai, Hangjun Che, Baicheng Pan, Man-Fai Leung, Cheng Liu, Shiping Wen Hit paper breakdown →	2024	57
6	A nonnegative matrix factorization algorithm based on a discrete-time projection neural network Neural Networks ·Hangjun Che, Jun Wang	2018	56
7	Low-Rank Tensor Regularized Graph Fuzzy Learning for Multi-View Data Processing IEEE Transactions on Consumer Electronics ·Baicheng Pan, Chuandong Li, Hangjun Che, Man-Fai Leung, Keping Yu	2023	52
8	Nonconvex low-rank tensor approximation with graph and consistent regularizations for multi-view subspace learning Neural Networks ·Baicheng Pan, Chuandong Li, Hangjun Che	2023	44
9	Tensor-Based Adaptive Consensus Graph Learning for Multi-View Clustering IEEE Transactions on Consumer Electronics ·Wei Guo, Hangjun Che, Man-Fai Leung	2024	43
10	Data-driven stock forecasting models based on neural networks: A review Information Fusion ·Wuzhida Bao, Yuting Cao, Yin Yang, Hangjun Che, Junjian Huang, Shiping Wen	2024	41
11	Tensor Factorization With Sparse and Graph Regularization for Fake News Detection on Social Networks IEEE Transactions on Computational Social Systems ·Hangjun Che, Baicheng Pan, Man-Fai Leung, Yuting Cao, Zheng Yan	2023	41
12	A metaheuristic-based framework for index tracking with practical constraints Complex & Intelligent Systems ·Man-Chung Yuen, Sin-Chun Ng, Man-Fai Leung, Hangjun Che	2021	39
13	Task Assignment for Multivehicle Systems Based on Collaborative Neurodynamic Optimization IEEE Transactions on Neural Networks and Learning Systems ·Jiasen Wang, Jun Wang, Hangjun Che	2019	37
14	Self-Guided Partial Graph Propagation for Incomplete Multiview Clustering IEEE Transactions on Neural Networks and Learning Systems ·Cheng Liu, Rui Li, Si Wu, Hangjun Che, Dazhi Jiang, Zhiwen Yu, Hau−San Wong	2023	37
15	Centric graph regularized log-norm sparse non-negative matrix factorization for multi-view clustering Signal Processing ·Yuzhu Dong, Hangjun Che, Man-Fai Leung, Cheng Liu, Zheng Yan	2023	36
16	A recurrent neural network for optimal real-time price in smart grid Neurocomputing ·Xing He, Tingwen Huang, Chuandong Li, Hangjun Che, Zhao Yang Dong	2014	36
17	Tensor-based unsupervised feature selection for error-robust handling of unbalanced incomplete multi-view data Information Fusion ·Xuanhao Yang, Hangjun Che, Man-Fai Leung	2024	33
18	Adaptive graph nonnegative matrix factorization with the self-paced regularization Applied Intelligence ·Xuanhao Yang, Hangjun Che, Man-Fai Leung, Cheng Liu	2022	30
19	Cardinality-constrained portfolio selection via two-timescale duplex neurodynamic optimization Neural Networks ·Man-Fai Leung, Jun Wang, Hangjun Che	2022	30
20	Graph non-negative matrix factorization with alternative smoothed $$L_0$$ regularizations Neural Computing and Applications ·Keyi Chen, Hangjun Che, Xinqi Li, Man-Fai Leung	2022	30

About Hangjun Che

Hangjun Che is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Computational Mechanics, Signal Processing and Urban Studies, having authored 79 papers that have together received 1.4k indexed citations. Recurring topics across this work include Face and Expression Recognition (29 papers), Neural Networks and Applications (15 papers), Sparse and Compressive Sensing Techniques (14 papers), Advanced Computing and Algorithms (8 papers), Tensor decomposition and applications (8 papers), Image Retrieval and Classification Techniques (7 papers), Blind Source Separation Techniques (5 papers) and Remote-Sensing Image Classification (5 papers). The work is most often cited by research in Computational Mathematics (95 citations), Computer Vision and Pattern Recognition (564 citations), Artificial Intelligence (615 citations), Urban Studies (64 citations) and Signal Processing (109 citations). Hangjun Che has collaborated with scholars based in China, United Kingdom and Hong Kong. Frequent co-authors include Jun Wang, Man-Fai Leung, Cheng Liu, Chuandong Li, Xing He, Shiping Wen, Tingwen Huang, Chenglu Li, Zheng Yan and Gang Feng. Their work appears in journals such as Neural Networks, IEEE Transactions on Neural Networks and Learning Systems, Information Fusion, IEEE Transactions on Consumer Electronics and IEEE Transactions on Computational Social Systems.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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