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Qiang Cheng
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Publications
- 2023
- [j90]Chong Peng, Xingrong Hou, Yongyong Chen, Zhao Kang, Chenglizhao Chen, Qiang Cheng:
Global and local similarity learning in multi-kernel space for nonnegative matrix factorization. Knowl. Based Syst. 279: 110946 (2023) - 2022
- [j80]Chong Peng, Zhilu Zhang, Chenglizhao Chen, Zhao Kang, Qiang Cheng:
Two-dimensional semi-nonnegative matrix factorization for clustering. Inf. Sci. 590: 106-141 (2022) - [j79]Chong Peng, Yiqun Zhang, Yongyong Chen, Zhao Kang, Chenglizhao Chen, Qiang Cheng:
Log-based sparse nonnegative matrix factorization for data representation. Knowl. Based Syst. 251: 109127 (2022) - [j78]Chong Peng, Jing Zhang, Yongyong Chen, Xin Xing, Chenglizhao Chen, Zhao Kang, Li Guo, Qiang Cheng:
Preserving bilateral view structural information for subspace clustering. Knowl. Based Syst. 258: 109915 (2022) - [j74]Chong Peng, Yang Liu, Kehan Kang, Yongyong Chen, Xinxing Wu, Andrew Cheng, Zhao Kang, Chenglizhao Chen, Qiang Cheng:
Hyperspectral Image Denoising Using Nonconvex Local Low-Rank and Sparse Separation With Spatial-Spectral Total Variation Regularization. IEEE Trans. Geosci. Remote. Sens. 60: 1-17 (2022) - [i41]Chong Peng, Yang Liu, Yongyong Chen, Xinxin Wu, Andrew Cheng, Zhao Kang, Chenglizhao Chen, Qiang Cheng:
Hyperspectral Image Denoising Using Non-convex Local Low-rank and Sparse Separation with Spatial-Spectral Total Variation Regularization. CoRR abs/2201.02812 (2022) - [i40]Chong Peng, Yiqun Zhang, Yongyong Chen, Zhao Kang, Chenglizhao Chen, Qiang Cheng:
Log-based Sparse Nonnegative Matrix Factorization for Data Representation. CoRR abs/2204.10647 (2022) - 2021
- [j70]Chong Peng, Zhilu Zhang, Zhao Kang, Chenglizhao Chen, Qiang Cheng:
Nonnegative matrix factorization with local similarity learning. Inf. Sci. 562: 325-346 (2021) - [j68]Chong Peng, Yang Liu, Xin Zhang, Zhao Kang, Yongyong Chen, Chenglizhao Chen, Qiang Cheng:
Learning discriminative representation for image classification. Knowl. Based Syst. 233: 107517 (2021) - [j67]Zhao Kang, Chong Peng, Qiang Cheng, Xinwang Liu, Xi Peng, Zenglin Xu, Ling Tian:
Structured graph learning for clustering and semi-supervised classification. Pattern Recognit. 110: 107627 (2021) - [j66]Chong Peng, Qian Zhang, Zhao Kang, Chenglizhao Chen, Qiang Cheng:
Kernel two-dimensional ridge regression for subspace clustering. Pattern Recognit. 113: 107749 (2021) - 2020
- [j56]Chong Peng, Yongyong Chen, Zhao Kang, Chenglizhao Chen, Qiang Cheng:
Robust principal component analysis: A factorization-based approach with linear complexity. Inf. Sci. 513: 581-599 (2020) - [i27]Chong Peng, Zhilu Zhang, Zhao Kang, Chenglizhao Chen, Qiang Cheng:
Two-Dimensional Semi-Nonnegative Matrix Factorization for Clustering. CoRR abs/2005.09229 (2020) - [i26]Zhao Kang, Chong Peng, Qiang Cheng, Xinwang Liu, Xi Peng, Zenglin Xu, Ling Tian:
Structured Graph Learning for Clustering and Semi-supervised Classification. CoRR abs/2008.13429 (2020) - [i23]Chong Peng, Qian Zhang, Zhao Kang, Chenglizhao Chen, Qiang Cheng:
Kernel Two-Dimensional Ridge Regression for Subspace Clustering. CoRR abs/2011.01477 (2020) - 2019
- [c52]Chong Peng, Chenglizhao Chen, Zhao Kang, Jianbo Li, Qiang Cheng:
RES-PCA: A Scalable Approach to Recovering Low-Rank Matrices. CVPR 2019: 7317-7325 - [i18]Chong Peng, Chenglizhao Chen, Zhao Kang, Jianbo Li, Qiang Cheng:
RES-PCA: A Scalable Approach to Recovering Low-rank Matrices. CoRR abs/1904.07497 (2019) - [i15]Chong Peng, Zhao Kang, Chenglizhao Chen, Qiang Cheng:
Nonnegative Matrix Factorization with Local Similarity Learning. CoRR abs/1907.04150 (2019) - 2018
- [j37]Chong Peng, Zhao Kang, Shuting Cai, Qiang Cheng:
Integrate and Conquer: Double-Sided Two-Dimensional k-Means Via Integrating of Projection and Manifold Construction. ACM Trans. Intell. Syst. Technol. 9(5): 57:1-57:25 (2018) - [c49]Zhao Kang, Chong Peng, Qiang Cheng, Zenglin Xu:
Unified Spectral Clustering With Optimal Graph. AAAI 2018: 3366-3373 - 2017
- [j35]Chong Peng, Zhao Kang, Qiang Cheng:
Integrating feature and graph learning with low-rank representation. Neurocomputing 249: 106-116 (2017) - [j34]Zhao Kang, Chong Peng, Qiang Cheng:
Kernel-driven similarity learning. Neurocomputing 267: 210-219 (2017) - [j33]Chong Peng, Zhao Kang, Fei Xu, Yongyong Chen, Qiang Cheng:
Image Projection Ridge Regression for Subspace Clustering. IEEE Signal Process. Lett. 24(7): 991-995 (2017) - [j31]Chong Peng, Zhao Kang, Yunhong Hu, Jie Cheng, Qiang Cheng:
Nonnegative Matrix Factorization with Integrated Graph and Feature Learning. ACM Trans. Intell. Syst. Technol. 8(3): 42:1-42:29 (2017) - [j30]Chong Peng, Zhao Kang, Yunhong Hu, Jie Cheng, Qiang Cheng:
Robust Graph Regularized Nonnegative Matrix Factorization for Clustering. ACM Trans. Knowl. Discov. Data 11(3): 33:1-33:30 (2017) - [c48]Zhao Kang, Chong Peng, Qiang Cheng:
Twin Learning for Similarity and Clustering: A Unified Kernel Approach. AAAI 2017: 2080-2086 - [c47]Chong Peng, Zhao Kang, Qiang Cheng:
Subspace Clustering via Variance Regularized Ridge Regression. CVPR 2017: 682-691 - [c46]Zhao Kang, Chong Peng, Ming Yang, Qiang Cheng:
Exploiting Nonlinear Relationships for Top-N Recommender Systems. ICBK 2017: 49-56 - [c45]Zhao Kang, Chong Peng, Qiang Cheng:
Clustering with Adaptive Manifold Structure Learning. ICDE 2017: 79-82 - [i9]Zhao Kang, Chong Peng, Qiang Cheng:
Twin Learning for Similarity and Clustering: A Unified Kernel Approach. CoRR abs/1705.00678 (2017) - [i8]Zhao Kang, Chong Peng, Qiang Cheng, Zenglin Xu:
Unified Spectral Clustering with Optimal Graph. CoRR abs/1711.04258 (2017) - 2016
- [j26]Chong Peng, Zhao Kang, Ming Yang, Qiang Cheng:
Feature Selection Embedded Subspace Clustering. IEEE Signal Process. Lett. 23(7): 1018-1022 (2016) - [c42]Zhao Kang, Chong Peng, Qiang Cheng:
Top-N Recommender System via Matrix Completion. AAAI 2016: 179-185 - [c40]Zhao Kang, Chong Peng, Ming Yang, Qiang Cheng:
Top-N Recommendation on Graphs. CIKM 2016: 2101-2106 - [c39]Chong Peng, Zhao Kang, Ming Yang, Qiang Cheng:
RAP: Scalable RPCA for Low-rank Matrix Recovery. CIKM 2016: 2113-2118 - [c38]Chong Peng, Zhao Kang, Qiang Cheng:
A Fast Factorization-Based Approach to Robust PCA. ICDM 2016: 1137-1142 - [c37]Zhao Kang, Qiang Cheng:
Top-N Recommendation with Novel Rank Approximation. SDM 2016: 126-134 - [i7]Zhao Kang, Chong Peng, Qiang Cheng:
Top-N Recommender System via Matrix Completion. CoRR abs/1601.04800 (2016) - [i6]Zhao Kang, Qiang Cheng:
Top-N Recommendation with Novel Rank Approximation. CoRR abs/1602.07783 (2016) - [i5]Zhao Kang, Chong Peng, Ming Yang, Qiang Cheng:
Top-N Recommendation on Graphs. CoRR abs/1609.08264 (2016) - 2015
- [j24]Zhao Kang, Chong Peng, Jie Cheng, Qiang Cheng:
LogDet Rank Minimization with Application to Subspace Clustering. Comput. Intell. Neurosci. 2015: 824289:1-824289:10 (2015) - [j22]Zhao Kang, Chong Peng, Qiang Cheng:
Robust Subspace Clustering via Smoothed Rank Approximation. IEEE Signal Process. Lett. 22(11): 2088-2092 (2015) - [c36]Zhao Kang, Chong Peng, Qiang Cheng:
Robust Subspace Clustering via Tighter Rank Approximation. CIKM 2015: 393-401 - [c35]Zhao Kang, Chong Peng, Qiang Cheng:
Robust PCA Via Nonconvex Rank Approximation. ICDM 2015: 211-220 - [c34]Chong Peng, Zhao Kang, Huiqing Li, Qiang Cheng:
Subspace Clustering Using Log-determinant Rank Approximation. KDD 2015: 925-934 - [i4]Zhao Kang, Chong Peng, Jie Cheng, Qiang Cheng:
LogDet Rank Minimization with Application to Subspace Clustering. CoRR abs/1507.00908 (2015) - [i3]Zhao Kang, Chong Peng, Qiang Cheng:
Robust Subspace Clustering via Smoothed Rank Approximation. CoRR abs/1508.04467 (2015) - [i2]Zhao Kang, Chong Peng, Qiang Cheng:
Robust Subspace Clustering via Tighter Rank Approximation. CoRR abs/1510.08971 (2015) - [i1]Zhao Kang, Chong Peng, Qiang Cheng:
Robust PCA via Nonconvex Rank Approximation. CoRR abs/1511.05261 (2015)
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