Profiles by Google Scholar by DBLP
108: Balanced Multi-Relational Graph Clustering, Zhixiang Shen, Haolan He, Zhao Kang*, ACM Multimedia 2024(code here) (Undergraduate: Zhixiang Shen, Haolan He)
107: Fine-Grained Essential Tensor Learning for Robust Multi-View Spectral Clustering, Chong Peng, Kehan Kang, Yongyong Chen, Zhao Kang, Chenglizhao Chen, Qiang Cheng, IEEE Transactions on Image Processing, 2024.
106: Multi-view subspace clustering using drop out technique on points, Fatemeh Sadjadi, Mina Jamshidi, Zhao Kang, International Journal Of Machine Learning And Cybernetics 2024.
105: FCDS: Fusing Constituency and Dependency Syntax into Document-Level Relation Extraction, Xudong Zhu, Zhao Kang*, Bei Hui, COLING 2024 (code here) (Undergraduate: Xudong Zhu)
104: Upper Bounding Barlow Twins: A Novel Filter for Multi-Relational Clustering, Xiaowei Qian, Bingheng Li, Zhao Kang*, AAAI 2024 (code here) (Undergraduate: Xiaowei Qian)
103: PC-Conv: Unifying Homophily and Heterophily with Two-fold Filtering, Bingheng Li, Erlin Pan, Zhao Kang*, AAAI 2024 (code here)(Undergraduate: Bingheng Li)
102: Global and local similarity learning in multi-kernel space for nonnegative matrix factorization, Chong Peng, Xingrong Hou, Yongyong Chen, Zhao Kang, Chenglizhao Chen, Qiang Cheng, Knowledge-Based Systems, 2023.
101：Self-Attention-Based Reconstruction for Planetary Magnetic Field, Ziqian Yan, Zhao Kang*, Ling Tian, NCAA 2023 (code )(Undergraduate: Ziqian Yan)
100. High-order Multi-view Clustering for Generic Data, Erlin Pan, Zhao Kang*, Information Fusion, 2023 (code here)
99. Intensity-free Convolutional Temporal Point Process: Incorporating Local and Global Event Contexts, Wang-Tao Zhou, Zhao Kang, Ling Tian, Yi Su, Information Sciences, Jun. 2023 (code here)
98：Self-paced Principal Component Analysis, Zhao Kang, Hongfei Liu, Jiangxin Li, Xiaofeng Zhu, Ling Tian, Pattern Recognition，2023. (code here)
97：Label shift conditioned hybrid querying for deep active learning, Jiaqi Li, Haojia Kong, Gezheng Xu, Changjian Shui, Ruizhi Pu, Zhao Kang, Charles X Ling, Boyu Wang, Knowledge-Based Systems, 2023.
96：Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering, Erlin Pan，Zhao Kang*，ICML 2023. (code here)
95：TieFake: Title-Text Similarity and Emotion-Aware Fake News Detection, Quanjiang Guo，Zhao Kang*，Ling Tian, Zhouguo Chen, IJCNN 2023. (code here)
94: Contrastive graph clustering with adaptive filter, Xuanting Xie, Wenyu Chen, Zhao Kang*, Chong Peng, Expert Systems with Applications, 2023. (code here)
93: Document-level Relation Extraction with Cross-sentence Reasoning Graph, Hongfei Liu，Zhao Kang*，Lizong Zhang，Ling Tian, Fujun Hua, PAKDD 2023. (code here)
92：Spacecraft anomaly detection with attention temporal convolution networks ，Liang Liu，Ling Tian，Zhao Kang*，Tianqi Wan， Neural Computing and Applications, 2023.(code here)
91：Graph Learning for Attributed Graph Clustering，Xiaoran Zhang, Xuanting Xie，Zhao Kang*，Mathematics 2022. (code here)
90：基于组合赋权的暴恐转向风险预测研究，Xiaocui Zhao, Zhao Kang*, Ling Tian，Bei Hui, Xi Zeng，广西科学，2022.
89：Forecasting of Internet Public Opinion Trends in Major Emergencies in Omni-media Era, Xiaocui Zhao, Zhao Kang, Ling Tian, Guangsheng Zhang, Computer and Digitial Engineering,2022.
88: Preserving bilateral view structural information for subspace clustering, Chong Peng; Jing Zhang; Yongyong Chen; Xin Xing; Chenglizhao Chen; Zhao Kang; Li Guo; Qiang Cheng, Knowledge-Based Systems, 2022.
87：Hyperspectral Image Denoising Using Non-convex Local Low-rank and Sparse Separation with Spatial-Spectral Total Variation Regularization, Chong Peng, Yang Liu, Kehan Kang, Yongyong Chen, Xinxin Wu, Andrew Cheng, Zhao Kang, Chenglizhao Chen, Qiang Cheng. IEEE Transactions on Geoscience and Remote Sensing, 2022.
86: Structure-Preserving Graph Representation Learning, Ruiyi Fang, liangjian Wen, Zhao Kang*, and Jianzhuang Liu, ICDM 2022.(Accept rate 20%) (code) (Undergraduate: Ruiyi Fang)
85: Multilayer Graph Contrastive Clustering Network, Liang Liu, Zhao Kang*, JiaJia Ruan, Xixu He, Information Sciences, 2022.(code)
84: Spaks: Self-paced multiple kernel subspace clustering with feature smoothing regularization, Qian Zhang, Zhao Kang*, Zenglin Xu, Shudong Huang, Hongguang Fu, Knowledge-Based Systems, 2022.
83：Eliminating Gradient Conflict in Reference-based Line-Art Colorization，Zekun Li; Zhengyang Geng; Zhao Kang*; Wenyu Chen; Yibo Yang, ECCV 2022. (Accept rate 28%)(Code )(Undergraduate: Zekun Li)
82：User Identity Identification Technologies and Challenges for Network Security Governance，Guangsheng Zhang; Zhao Kang; Ling Tian，电子科技大学学报, 2022.
81: Log-based Sparse Nonnegative Matrix Factorization for Data Representation, Chong Peng, Yiqun Zhang, Yongyong Chen, Zhao Kang, Chenglizhao Chen, Qiang Cheng, Knowledge-Based Systems, 2022.
80: Multi-graph Fusion for Dynamic Graph Convolutional Network, Jiangzhang Gan, Rongyao Hu, Yujie Mo, Zhao Kang; Liang Peng, Yonghua Zhu, Xiaofeng Zhu, IEEE Transactions on Neural Networks and Learning Systems，2022.
79: Semi-supervised Classification Based on Transformed Learning, Zhao Kang, Liang Liu, Meng Han, 计算机研究与发展, 2022.
78: Scalable Multi-view Clustering with Graph Filtering, Liang Liu, Peng Cheng, Guangchun Luo, Zhao Kang*, Yonggang Luo, Sanchu Han, Neural Computing and Applications 2022. (Code )
77: Optimizing Piezoelectric Nanocomposites by High-Throughput Phase-Field Simulation and Machine Learning, Weixiong Li, Tiannan Yang, Changshu Liu, Yuhui Huang, Chunxu Chen, Hong Pan, Guangzhong Xie, Huiling Tai, Yadong Jiang, Yongjun Wu, Zhao Kang*, Long-Qing Chen, Yuanjie Su, Zijian Hong, Advanced Science 2022.
76: Two-dimensional semi-nonnegative matrix factorization for clustering, Chong Peng; Zhilu Zhang; Chenglizhao Chen; ZhaoKang; QiangCheng, Information Sciences 2022.
75: Multi-local feature relation network for few-shot learning, Li Ren, Guiduo Duan, Tianxi Huang, Zhao Kang, Neural Computing and Applications 2022.
74: Riemannian Manifold-based Multi-view Spectral Clustering , Linke Li, Zhao Kang*, Bo Long, 计算机工程 2022. (code) (Undergraduate: Linke Li)
73: Fine-grained Attributed Graph Clustering, Zhao Kang, Zhanyu Liu, Shirui Pan, Ling Tian, SDM 2022. (Accept rate 27.8%) (code)(Undergraduate: Zhanyu Liu)
72: Multi-view Contrastive Graph Clustering, Erlin Pan, Zhao Kang*, NeurIPS 2021. (Accept rate 26%) (code )
71: Graph Fusion Network for text classification, Yong Dai, Linjun Shou, Ming Gong, Xiaolin Xia, Zhao Kang, Zenglin Xu, Daxin Jiang, Knowledge-Based Systems, 2021.
70: Learning discriminative representation for image classification , Chong Peng, Yang Liu, Xin Zhang, Zhao Kang, Yongyong Chen, Chenglizhao Chen, Qiang Cheng, Knowledge-Based Systems, 2021.
69:Multi-view Attributed Graph Clustering , Zhiping Lin, Zhao Kang*, Lizong Zhang, Ling Tian, IEEE Transactions on Knowledge and Data Engineering, 2021, DOI: 10.1109/TKDE.2021.3101227.(code)
68:Self-supervised Consensus Representation Learning for Attributed Graph , Changshu Liu, Liangjian Wen, Zhao Kang*，Guangchun Luo, Ling Tian, ACM Multimedia, 2021. (Accept rate 27.9%) (code)
67: Smoothed Multi-View Subspace Clustering, Chen Peng, Liang Liu, Zhengrui Ma, Zhao Kang*, International Conference on Neural Computing for Advanced Applications, 2021. (code )
66: Pseudo-supervised Deep Subspace Clustering, Juncheng Lv, Zhao Kang*, Xiao Lu, Zenglin Xu, IEEE Transactions on Image Processing, 30, 5252-5263, 2021. (Code)
65: Graph Filter-based Multi-view Attributed Graph Clustering, Zhiping Lin, Zhao Kang*, IJCAI 2021. (Accept rate 13.9%) (Code)
64: Robust Deep K-Means: An Effective and Simple Method for Data Clustering , Shudong Huang, Zhao Kang, Zenglin Xu, and Quanhui Liu, Pattern Recognition, 2021.
63: Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view, Zhao Kang, Zhiping Lin, Xiaofeng Zhu, Wenbo Xu, IEEE Transactions on Cybernetics, 2021, DOI: 10.1109/TCYB.2021.3061660. (Code)
62: Nonnegative matrix factorization with local similarity learning, Chong Peng, Zhilu Zhang, Zhao Kang, Chenglizhao Chen, Qiang Cheng, Information Sciences, 2021.
61: Multi-view Subspace Clustering via Partition Fusion , Juncheng Lv; Zhao Kang*; Boyu Wang; Luping Ji; Zenglin Xu, Information Sciences, 2021. (code)
60:Domain Adaptation with Feature and Label Adversarial Networks , Peng Zhao, Wenhua Zang, Bin Liu, Zhao Kang*, Kun Bai, Kaizhu Huang, Zenglin Xu, Neurocomputing, 2021. (code)
59:Self-Paced Two-dimensional PCA , Jiangxin Li, Zhao Kang*, Chong Peng, Wenyu Chen, AAAI 2021, Feb 2-9. (Code)(Undergraduate: jiangxin Li)
58: Kernel Two-Dimensional Ridge Regression for Subspace Clustering , Chong Peng, Qian Zhang, Zhao Kang, Chenglizhao Chen, Qiang Cheng, Pattern Recognition, 2021.
57:Structured Graph Learning for Clustering and Semi-supervised Classification , Zhao Kang, Chong Peng, Qiang Cheng, Xinwang Liu, Xi Peng, Zenglin Xu, Ling Tian, Pattern Recognition, 2021. (Code )
56: Deep K-Means: A Simple and Effective Method for Data Clustering , Shudong Huang, Zhao Kang, Zenglin Xu, 2020 International Conference on Neural Computing for Advanced Applications.
55: Smooth Representation Semi-Supervised Classification , Xing Wang, Zhao Kang*, Computer Science, 2020. (In Chinese) (Code )(Undergraduate: Xing Wang)
54: Towards Clustering-friendly Representations: Subspace Clustering via Graph Filtering , Zhengrui Ma, Zhao Kang*, Guangchun Luo, Ling Tian, Wenyu Chen, ACM Multimedia, Seattle, October 2020. (Accept rate 27.8%) (Code )(Undergraduate: Zhengrui Ma)
53: Generalized Locally-linear embedding: A Neural Network Implementation , Xiao Lu, Zhao Kang*, Jiachun Tang, Shuang Xie and Yuanzhang Su, 2020 International Conference on Neural Computing for Advanced Applications. (Undergraduate: Jiachun Tang)
52: Relation-Guided Representation Learning , Zhao Kang*, Xiao Lu*, Jian Liang, Kun Bai, Zenglin Xu, Neural Networks, 2020. (Code)
51: Structure Learning with Similarity Preserving, Zhao Kang, Xiao Lu, Yiwei Lu, Chong Peng, Wenyu Chen, Zenglin Xu, Neural Networks, Vol 129, 138-148, Sep 2020. (Code) (Undergraduate: Yiwei Lu)
50: On Deep Unsupervised Active Learning , Changsheng Li, Handong Ma, Zhao Kang, Ye Yuan, Xiao-Yu Zhang, Guoren Wang, IJCAI 2020. (Accept rate 12.6%)
49: Large-scale Multi-view Subspace Clustering in Linear Time, Zhao Kang, Wangtao Zhou, Zhitong Zhao, Junming Shao, Meng Han, Zenglin Xu, Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), New York, Feb. 2020. (Code) (Accept rate 20.6%) (Undergraduate: Wangtao Zhou)
48: Common Spatial Pattern Reformulated for Regularizations in Brain-Computer Interfaces, Boyu Wang, Chi Man Wong, Zhao Kang, Feng Liu, Changjian Shui, Feng Wan, C. L. Philip Chen, IEEE Transactions on Cybernetics, 2020.
47: Exploring nonnegative and low-rank correlation for noise-resistant spectral clustering, Zheng Wang, Lin Zuo, Jing Ma, Si Chen, Jingjing Li, Zhao Kang, Lei Zhang, World Wide Web, 2020.
46: Regularized Nonnegative Matrix Factorization with Adaptive Local Structure Learning, Shudong Huang, Zenglin Xu, Zhao Kang, Yazhou Ren, Neurocomputing, 2020.
45: Partition Level Multiview Subspace Clustering, Zhao Kang; Xinjia Zhao; Chong Peng; Hongyuan Zhu; Joey Tianyi Zhou; Xi Peng; Wenyu Chen; Zenglin Xu, Neural Networks, Feb. 2020. (Code) (Undergraduate: Xinjia Zhao)
44: Single Image Dehazing via Compositional Adversarial Network, Hongyuan Zhu, Yi Cheng, Xi Peng, Joey Tianyi Zhou, Zhao Kang, Shijian Lu, Zhiwen Fang, Liyuan Li, Joo-Hwee Lim, IEEE Transactions on Cybernetics, 2019.
43: Multi-graph Fusion for Multi-view Spectral Clustering, Zhao Kang; Guoxin Shi; Shudong huang; Wenyu Chen; Xiaorong Pu; Joey Tianyi Zhou; Zenglin Xu, Knowledge-Based Systems, 2020. (Code) (Undergraduate: Guoxin Shi)
42: Auto-weighted Multi-view Co-clustering with Bipartite Graphs, Shudong Huang, Zenglin Xu, Ivor W. Tsang, Zhao Kang, Information Sciences 2020.
41: Robust Principal Component Analysis: A Factorization-Based Approach with Linear Complexity, Chong Peng, Yongyong Chen, Zhao Kang, Chenglizhao Chen, Qiang Cheng, Information Sciences 2019.
40: Latent Multi-view Semi-Supervised Classification, Xiaofan Bo*, Zhao Kang*, Zhitong Zhao, Wenyu Chen, Yuanzhang Su, The 11th Asian Conference on Machine Learning (ACML 2019), Nov.2019, Nagoya, Japan. (code) (Undergraduate: Xiaofan Bo)
39: Auto-weighted multi-view clustering via deep matrix decomposition, Shudong Huang, Zhao Kang, Zenglin Xu, Pattern Recognition, Volume 97, 2020.
38: Multiple Partitions Aligned Clustering, Zhao Kang, Zipeng Guo, Shudong Huang, Siying Wang, Wenyu Chen, Yuanzhang Su, Zenglin Xu, The 28th International Joint Conference on Artificial Intelligence(IJCAI-19), Aug. 2019, Macao, China. (Code)(Accept rate 17.9%) (Undergraduate: Zipeng Guo)
37: Clustering with Similarity Preserving, Zhao Kang, Honghui Xu, Boyu Wang, Hongyuan Zhu, Zenglin Xu, Neurocomputing, 2019. (Code)
36: RES-PCA: A Scalable Approach to Recovering Low-rank Matrices, C Peng, C Chen, Z Kang, J Li, Q Cheng, IEEE Conference on Computer Vision and Pattern Recognition (CVPR'2019). (Accept rate 25.2%)
35: Locality-constrained group lasso coding for microvessel image classification, J Chen, S Zhou, Z Kang, Q Wen, Pattern Recognition Letters, 2019.
34: Similarity Learning via Kernel Preserving Embedding, Zhao Kang; Yiwei Lu; Yuanzhang Su; Changsheng Li; Zenglin Xu, The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), Honolulu, Hawaii, Jan. 2019. (Code) (Accept rate 16.2%) (Undergraduate: Yiwei Lu)
33: Robust Graph Learning from Noisy Data, Zhao Kang, Haiqi Pan, Steven C.H. Hoi, Zenglin Xu, IEEE Transactions on Cybernetics, 2020 (5), 1833-1843. (Code) (Undergraduate: Haiqi Pan)
32: Two Birds with One Stone: Transforming and Generating Facial Images with Iterative GAN, Dan Ma, Bin Liu, Zhao Kang, Jiayu Zhou, Jianke Zhu, Zenglin Xu, Neurocomputing, 2019.
31: Auto-weighted Multi-view Clustering via Kernelized Graph Learning, Shudong Huang, Zhao Kang, Ivor W. Tsang, Zenglin Xu, Pattern Recognition, Volume 88, April 2019, Pages 174-184.
30: Low-rank Kernel Learning for Graph-based Clustering , Zhao Kang, Liangjian Wen, Wenyu Chen, Zenglin Xu , Knowledge-Based Systems, 2018. (Code)
29: Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification, Zhao Kang; Xiao Lu; Jinfeng Yi; Zenglin Xu, The 27th International Joint Conference on Artificial Intelligence(IJCAI-18), July. 2018, Stockholm, Sweden. (Code) (Accept rate 20.5%)
28: Robust Graph Learning for Semi-Supervised Classification, Haiqi Pan; Zhao Kang , International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC 2018), Hangzhou, China. (Undergraduate: Haiqi Pan)
27: Self-weighted Multi-View Clustering with Soft Capped Norm, Shudong Huang; Zhao Kang; Zenglin Xu, Knowledge-Based Systems, 2018.
26: Image Denoising via Improved Dictionary Learning with Global Structure and Local Similarity Preservations, Shuting Cai, Zhao Kang, Ming Yang, Xiaoming Xiong, Chone Peng, Mingqing Xiao, Symmetry 10 (5), 167.
25: Integrate and Conquer: Double-Sided Two-Dimensional K-Means Via Integrating of Projection and Manifold Construction, Chong Peng; Zhao Kang; Shuting Cai; Qiang Cheng, ACM Transactions on Intelligent Systems and Technology (ACM TIST), 2018.
24: Unified Spectral Clustering with Optimal Graph, Zhao Kang; Chong Peng; Qiang Cheng; Zenglin Xu, The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), New Orleans, Lousiana, Feb. 2018. (Code) (Accept rate 24.5%)
23: Kernel-driven Similarity Learning, Zhao Kang; Chong Peng; Qiang Cheng, Neurocomputing, Volume 267, 6 December 2017, Pages 210-219. (code)
22: Exploiting Nonlinear Relationships for Top-N Recommender Systems, Zhao Kang; Chong Peng; Ming Yang, Qiang Cheng, The 8th IEEE International Conference on Big Knowledge, Hefei, China, August. 2017. (Accept rate 27%)
21: On identifiability of 3-tensors of multilinear rank (1, Lr, Lr), Ming Yang, DunRen Che, Wen Liu, Zhao Kang, Chong Peng, Mingqing Xiao, Qiang Cheng, Big Data and Information Analytics (BDIA), American Institute of Mathematical Sciences, Vol. 1, no. 4, October 2016.
20: Image Projection Ridge Regression for Subspace Clustering, Chong Peng; Zhao Kang; Fei Xu; Yongyong Chen; Qiang Cheng, IEEE Signal Processing Letters (IEEE SPL), 2017.
19: Subspace Clustering via Variance Regularized Ridge Regression, Chong Peng; Zhao Kang; Qiang Cheng, The Thirtieth IEEE/CVF Conference on Computer Vision and PatternRecognition (CVPR 2017), Honolulu, Hawaii, July, 2017. (Accept rate 29%)
18: Integrating Feature and Graph Learning with Low-Rank Representation, Chong Peng; Zhao Kang; Qiang Cheng, Neurocomputing, 2017.
17: Clustering with Adaptive Manifold Structure Learning, Zhao Kang; Chong Peng; Qiang Cheng, The 33rd IEEE International Conference on Data Engineering (ICDE 2017), San Diego, USA, April. 2017. (Code) (Accept rate 28.9%)
16: Twin Learning for Similarity and Clustering: A Unified Kernel Approach, Zhao Kang; Chong Peng; Qiang Cheng, The Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), San Francisco, California USA, Feb. 2017. (Accept rate 24.6%). Code is here.
15: Robust Graph Regularized Nonnegative Matrix Factorization for Clustering, Chong Peng; Zhao Kang; Yunhong Hu; Qiang Cheng, ACM Transactions on Knowledge Discovery from Data (ACM TKDD), Volume 11 Issue 3, Article No. 33, March 2017.
14: A Fast Factorization-based Approach to Robust Principal Component Analysis, Chong Peng; Zhao Kang; Qiang Cheng, The IEEE International Conference on Data Mining series (ICDM 2016), Barcelona, Spain, Dec. 2016. (Accept rate 19.6%). Code is here.
13: Nonnegative Matrix Factorization with Integrated Graph and Feature Learning, Chong Peng; Zhao Kang; Yunhong Hu; Qiang Cheng, ACM Transactions on Intelligent Systems and Technology (ACM TIST), Vol. 8, No. 3, Article 42, February 2017.
12: Top-N Recommendation on Graphs, Zhao Kang; Chong Peng; Ming Yang, Qiang Cheng, The 25th ACM Int. Conf. on Information and Knowledge Management (CIKM 2016), Indianapolis, United States, Oct. 2016. (Accept rate 23.2%). Code is available here.
11: RAP: Scalable RPCA for Low-rank Matrix Recovery, Chong Peng; Zhao Kang; Ming Yang, Qiang Cheng, The 25th ACM Int. Conf. on Information and Knowledge Management (CIKM 2016), Indianapolis, United States, Oct. 2016. (Accept rate 23.2%).
10: Feature Selection Embedded Subspace Clustering, Chong Peng; Zhao Kang; Ming Yang, Qiang Cheng, IEEE Signal Processing Letters (IEEE SPL) 23(7), 1018-1022, 2016. The code can be downloaded here.
9: Top-N recommendation with novel rank approximation, Zhao Kang and Qiang Cheng, 2016 SIAM Int. Conf. on Data Mining (SDM 2016), Miami, FL, May. 2016. (Accept rate 26%). The code is available here.
8: Top-N Recommender System via Matrix Completion, Zhao Kang, Chong Peng, and Qiang Cheng, The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), Phoenix, Arizona, USA, Feb. 2016. (Accept rate 26%). Code is here.
7: Robust PCA Via Nonconvex Rank Approximation, Zhao Kang, Chong Peng, and Qiang Cheng, The IEEE International Conference on Data Mining series (ICDM 2015), Atlantic, NJ, USA, Nov. 2015. (Accepted for oral presentation, accept rate 68/807=8.4%). (The code is available here)
6: Robust Subspace Clustering via Tighter Rank Approximation, Zhao Kang, Chong Peng, and Qiang Cheng, The 24th ACM Int. Conf. on Information and Knowledge Management (CIKM 2015), Melbourne, Australia, Oct. 2015. (Accepted for oral presentation, accept rate 17.98%). The code is available here.
5: Subspace clustering using log-determinant rank approximation, Chong Peng, Zhao Kang, Huiqing Li, Qiang Cheng, The 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2015), Sydney, Australia, Aug. 2015. (Accepted for oral presentation , accept rate 19.4%). Code is available here.
4: Robust Subspace Clustering via Smoothed Rank Approximation , Zhao Kang, Chong Peng, and Qiang Cheng, IEEE Signal Processing Letters (IEEE SPL) 22 (11), 2088-2092.(The Code can be downloaded from here)
3: LogDet Rank Minimization with Application to Subspace Clustering, Zhao Kang, Chong Peng, Jie Cheng and Qiang Cheng, Computational Intelligence and Neuroscience, Volume 2015 (2015).
2: High-resolution gamma-ray spectroscopy with a microwave-multiplexed transition-edge sensor array, Omid Noroozian, John AB Mates, Douglas A Bennett, Justus A Brevik, Joseph W Fowler, Jiansong Gao, Gene C Hilton, Robert D Horansky, Kent D Irwin, Zhao Kang, Daniel R Schmidt, Leila R Vale, Joel N Ullom, Applied Physics Letters 103, 202602 (2013).
1: Neutralino Reconstruction at the LHC from Decay-frame Kinematics, Zhao Kang, Nicholas Kersting, Sabine Kraml, Are R Raklev, Martin J White, Eur. Phys. J. C (2010) 70: 271.