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彭冲,2012年获金沙城娱乐场统计学学士学位,2017年获南伊利诺伊大学计算机科学博士学位,2018年加入金沙城娱乐场。现为金沙城娱乐场助理教授,硕士生导师,入选金沙城娱乐场青年卓越人才。研究领域为机器学习,数据挖掘,人工智能,计算机视觉,在这些领域的国际顶级会议和学术期刊,包括KDD,CVPR,AAAI,ICDE,ICDM,CIKM,ACM TIST,TKDD,IEEE TGRS等发表学术论文30余篇。目前主持国家自然科学基金(青年)和山东省自然科学基金(青年)各一项。担任IEEE Trans. Cybernetics, Knowledge and Information Science, Information Sciences, IEEE Access, Neurocomputing等国际期刊审稿人。

联系方式:pchong1991@163.com

个人学术主页:http://www.escience.cn/people/cpeng/index.html 

 

项目:

1. 面向大规模二维数据的岭回归子空间聚类算法的研究,国家自然科学基金青年项目,项目编号 61806106,2019.01-2021.12 主持

2. 面向二维数据聚类的二维非负矩阵分解算法研究,山东省自然科学基金青年项目,项目编号 ZR2019QF009, 2019.07-2022.06 主持

 

部分论文:

1. RES-PCA: A Scalable Approach to Recovering Low-Rank Matrices, Chong Peng, Zhao Kang, Chenglizhao Chen, Jianbo Li, Qiang Cheng. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR-2019): 2019.06

2. Structure-aware Adaptive Diffusion for Video Saliency Detection, Chenglizhao Chen, Guotao Wang, Chong Peng *(通讯). IEEE Access: 2019

3. Integrate and conquer: Double-sided two-dimensional k-means via integrating of projection and manifold construction, Chong Peng, Zhao Kang, Shuting Cai, and Qiang Cheng. ACM Transactions on Intelligent Systems and Technology (TIST): 2018 ,9(5) ,57

4. Image denoising via improved dictionary learning with global structure and local similarity preservations, Shuting Cai, Zhao Kang, Ming Yang, Xiaoming Xiong, Chong Peng*(通讯), and Mingqing Xiao. Symmetry:2018 ,10(5) ,167

5. Unified spectral clustering with optimal graph, Zhao Kang, Chong Peng, Qiang Cheng, and Zenglin Xu. Thirty-Second AAAI Conference on Artificial Intelligence: 2018

6. Clustering with Adaptive Manifold Structure Learning, Zhao Kang, Chong Peng, and Qiang Cheng. IEEE International Conference on Data Engineering (ICDE):2017

7. Exploiting Nonlinear Relationships for Top-N Recommender Systems, Zhao Kang, Chong Peng, Ming Yang, and Qiang Cheng. The 8th IEEE International Conference on Big Knowledge: 2017

8. Denoising of Hyperspectral Image Using Low-Rank Matrix Factorization, Fei Xu, Yongyong Chen, Chong Peng, Yongli Wang, Xuefeng Liu and Guoping He. IEEE Geoscience & Remote Sensing Letters: 2017

9. Denoising of Hyperspectral Images Using Nonconvex Low Rank Matrix Approximation, Yongyong Chen, Yanwen Guo, Yongli Wang, Dong Wang, Chong Peng, and Guoping He. IEEE Transactions on Geoscience & Remote Sensing: 2017

10. A supervised learning model for high-dimensional and large-scale data, Chong Peng, Jie Cheng, and Qiang Cheng. ACM Transactions on Intelligent Systems and Technology (TIST): 2017 ,8(2) ,30

11. Image projection ridge regression for subspace clustering, Chong Peng, Zhao Kang, Fei Xu, Yongyong Chen, and Qiang Cheng. IEEE Signal Processing Letters:2017 ,24(7) ,991--995

12. RAP: Scalable RPCA for Low-rank Matrix Recovery, Chong Peng, Zhao Kang, Ming Yang, and Qiang Cheng. The 25th ACM Int. Conf. on Information and Knowledge Management (CIKM 2016): 2016

13. Robust Subspace Clustering via Smoothed Rank Approximation, Zhao Kang, Chong Peng, and Qiang Cheng. IEEE Signal Processing Letters: 2015 ,22(11) ,2088--2092

14. Robust PCA via Nonconvex Rank Approximation, Zhao Kang, Chong Peng, and Qiang Cheng. IEEE International Conference on Data Mining (ICDM): 2015

15. Subspace clustering via variance regularized ridge regression, Chong Peng, Zhao Kang, and Qiang Cheng. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR-2017): 2017 ,2931--2940

16. Robust graph regularized nonnegative matrix factorization for clustering, Chong Peng, Zhao Kang, Yunhong Hu, Jie Cheng, and Qiang Cheng. ACM Transactions on Knowledge Discovery from Data (TKDD): 2017 ,11(3) ,33

17. Nonnegative matrix factorization with integrated graph and feature learning, Chong Peng, Zhao Kang, Yunhong Hu, and Qiang Cheng. ACM Transactions on Intelligent Systems and Technology (TIST): 2017 ,8(3) ,42

18. Twin learning for similarity and clustering: A unified kernel approach, Zhao Kang, Chong Peng, and Qiang Cheng. Thirty-First AAAI Conference on Artificial Intelligence: 2017

19. Integrating feature and graph learning with low-rank representation, Chong Peng, Zhao Kang, and Qiang Cheng. Neurocomputing: 2017 ,249 ,106--116

20. Kernel-driven similarity learning, Zhao Kang, Chong Peng, and Qiang Cheng. Neurocomputing: 2017 ,267 ,210--219

21. A fast factorization-based approach to robust PCA, Chong Peng, Zhao Kang, and Qiang Cheng. 2016 IEEE 16th International Conference on Data Mining (ICDM): 2016 ,1137--1142

22. ON IDENTIFIABILITY OF 3-TENSORS OF MULTILINEAR RANK (1; Lr; Lr), Ming Yang, Dunren Che, Wen Liu, Zhao Kang, Chong Peng, Mingqing Xiao, and Qiang Cheng. Big Data and Information Analytics: 2016

23. Top-n recommendation on graphs, Zhao Kang, Chong Peng, Ming Yang, and Qiang Cheng. The 25th ACM International on Conference on Information and Knowledge Management (CCF-B): 2016 ,2101--2106

24. Feature selection embedded subspace clustering, Chong Peng, Zhao Kang, Ming Yang, and Qiang Cheng. IEEE Signal Processing Letters: 2016 ,23(7),1018--1022

25. Top-n recommender system via matrix completion, Zhao Kang, Chong Peng, and Qiang Cheng. Thirtieth AAAI Conference on Artificial Intelligence (CCF-A):2016

26. Robust subspace clustering via tighter rank approximation, Zhao Kang, Chong Peng, and Qiang Cheng. Proceedings of the 24th ACM International on Conference on Information and Knowledge Management: 2015 ,393--401

27. Subspace clustering using log-determinant rank approximation, Chong Peng, Zhao Kang, Huiqing Li, and Qiang Cheng. Proceedings of the 21th ACM SIGKDD international conference on Knowledge Discovery and Data Mining: 2015 ,925--934

28. Logdet rank minimization with application to subspace clustering, Zhao Kang, Chong Peng, Jie Cheng, Qiang Cheng. Computational intelligence and neuroscience: 2015,2015 ,68

 


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