王政副教授-光电与智能研究院(iOPEN)—

副教授

您当前所在位置是: 首页 >> 师资队伍 >> 副教授 >> 正文

王政副教授

个人简介

王政：副教授硕士生导师

学术主页：[谷歌学术] [DBLP]

研究方向：多模态认知计算

联系地址：陕西省西安市碑林区友谊西路127号西北工业大学

邮政编码：710072

电子邮箱：zhengwangml AT gmail.com

人物简介：王政，1991年生，工学博士，西北工业大学光电与智能研究院副教授，硕导。2017和2021年分别获得安徽大学硕士和西北工业大学博士学位。近五年发表学术论文40余篇，其中中科院一区期刊20余篇，包括人工智能国际期刊IEEE Trans-PAMI、IEEE Trans-NNLS、IEEE Trans-CYB、IEEE Trans-IP等，以及CCF A类期刊会议4篇，包括IEEE Trans-KDE、IJCAI。

教育背景与工作经历

2017-2021	西北工业大学	博士
2021-2023	西安交通大学	博士后
2023至今	西北工业大学	副教授

学术成果

Representative works (last three years):

Z. Wang, F. Nie, C. Zhang, R. Wang and X. Li, "Worst-case Discriminative Feature Learning via Max-Min Ratio Analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence,in press, 2023. [BibTeX]|[PDF]

@ARTICLE{10275043,
                                        author={Wang, Zheng and Nie, Feiping and Zhang, Canyu and Wang, Rong and Li, Xuelong},
                                        journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
                                        title={Worst-case Discriminative Feature Learning via Max-Min Ratio Analysis},
                                        year={2023},
                                        volume={},
                                        number={},
                                        pages={1-18},
                                        doi={10.1109/TPAMI.2023.3323453}}

F. Nie, Z. Wang(学一), R. Wang, Z. Wang and X. Li, "Towards Robust Discriminative Projections Learning via Non-greedy L2,1-Norm MinMax," IEEE Transactions on Pattern Analysis and Machine Intelligence, (IF: 17.861), 2020. [BibTeX]|[PDF]

@article{nie2019towards,
                                        title={Towards Robust Discriminative Projections Learning via Non-greedy $$\{$l$\}$ \_ $\{$2, 1$\}$ $-Norm MinMax},
                                        author={Nie, Feiping and Wang, Zheng and Wang, Rong and Wang, Zhen and Li, Xuelong},
                                        journal={IEEE transactions on pattern analysis and machine intelligence},
                                        year={2019},
                                        publisher={IEEE}
                                      }

Z. Wang, F. Nie, H. Wang, H. Huang and F. Wang, "Toward Robust Discriminative Projections Learning Against Adversarial Patch Attacks," IEEE Transactions on Neural Networks and Learning Systems, in press, 2023. [BibTeX]|[PDF]

@ARTICLE{10286885,
                                    author={Wang, Zheng and Nie, Feiping and Wang, Hua and Huang, Heng and Wang, Fei},
                                    journal={IEEE Transactions on Neural Networks and Learning Systems},
                                    title={Toward Robust Discriminative Projections Learning Against Adversarial Patch Attacks},
                                    year={2023},
                                    volume={},
                                    number={},
                                    pages={1-15},
                                    doi={10.1109/TNNLS.2023.3321606}}

Z. Wang, Y. Yuan, R. Wang, F. Nie, Q. Huang and X. Li, "Pseudo-Label Guided Structural Discriminative Subspace Learning for Unsupervised Feature Selection," IEEE Transactions on Neural Networks and Learning Systems, in press, 2023. [BibTeX]|[PDF]

@ARTICLE{10272670,
                                        author={Wang, Zheng and Yuan, Yongjin and Wang, Rong and Nie, Feiping and Huang, Qinghua and Li, Xuelong},
                                        journal={IEEE Transactions on Neural Networks and Learning Systems},
                                        title={Pseudo-Label Guided Structural Discriminative Subspace Learning for Unsupervised Feature Selection},
                                        year={2023},
                                        volume={},
                                        number={},
                                        pages={1-15},
                                        doi={10.1109/TNNLS.2023.3319372}}

Z. Wang, Q. Li, F. Nie, R. Wang, F. Wang and X. Li, "Efficient Local Coherent Structure Learning via Self-Evolution Bipartite Graph," IEEE Transactions on Cybernetics, in press, 2023. [BibTeX]|[PDF]

@ARTICLE{10297307,
                        author={Wang, Zheng and Li, Qi and Nie, Feiping and Wang, Rong and Wang, Fei and Li, Xuelong},
                        journal={IEEE Transactions on Cybernetics},
                        title={Efficient Local Coherent Structure Learning via Self-Evolution Bipartite Graph},
                        year={2023},
                        volume={},
                        number={},
                        pages={1-12},
                        doi={10.1109/TCYB.2023.3321843}}

Z. Wang, D. Wu, R. Wang, F. Nie and F. Wang, "Joint Anchor Graph Embedding and Discrete Feature Scoring for Unsupervised Feature Selection," IEEE Transactions on Neural Networks and Learning Systems, in press, 2022. [BibTeX]|[PDF]

@ARTICLE{9960718,
                                        author={Wang, Zheng and Wu, Dongming and Wang, Rong and Nie, Feiping and Wang, Fei},
                                        journal={IEEE Transactions on Neural Networks and Learning Systems},
                                        title={Joint Anchor Graph Embedding and Discrete Feature Scoring for Unsupervised Feature Selection},
                                        year={2022},
                                        volume={},
                                        number={},
                                        pages={1-14},
                                        doi={10.1109/TNNLS.2022.3222466}}

Z. Wang, F. Nie, L. Tian, R. Wang and X. Li, "Discriminative feature selection via a structured sparse subspace learning module," Proceedings of the International Joint Conference on Artificial Intelligence - Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI, CCF A), in press, 2020. [BibTeX]|[PDF]

@inproceedings{wang2020discriminative,
                                        title={Discriminative feature selection via a structured sparse subspace learning module},
                                        author={Wang, Zheng and Nie, Feiping and Tian, Lai and Wang, Rong and Li, Xuelong},
                                        booktitle={Proc. Twenty-Ninth Int. Joint Conf. Artif. Intell.},
                                        pages={3009--3015},
                                        year={2020}
                                      }

Z. Wang, Q. Li, H. Zhao and F. Nie, "Simultaneous local clustering and unsupervised feature selection via strong space constraint," Pattern Recognition, in press, 2023. [BibTeX]|[PDF]

@article{WANG2023109718,
                                        title = {Simultaneous local clustering and unsupervised feature selection via strong space constraint},
                                        journal = {Pattern Recognition},
                                        volume = {142},
                                        pages = {109718},
                                        year = {2023},
                                        issn = {0031-3203},
                                        doi = {https://doi.org/10.1016/j.patcog.2023.109718},
                                        url = {https://www.sciencedirect.com/science/article/pii/S0031320323004168},
                                        author = {Zheng Wang and Qi Li and Haifeng Zhao and Feiping Nie},
                                        keywords = {Unsupervised feature selection, -Norm constraint optimization, Local structure learning},
                                        abstract = {Clustering is a fashion method applied in machine learning tasks. However, high dimensional data brings many
                                        obstacles for clustering approaches. To address such a problem, the unsupervised feature selection (UFS) method can be
                                        incorporated into clustering to reduce dimensionality. In general, most of the UFS methods adopt ℓ2,1-norm for subspace
                                        sparsity learning. However, its sparsity highly relies on the setting of trade-off parameter, which may lead to
                                        instability of ranking results and the difficulty in obtaining the optimal solution of projection matrix. In this paper,
                                        we propose to directly learn an absolutely row-sparsity subspace via the ℓ2,0-norm constraint, called Sparse constraint
                                        and Local learning for Unsupervised Feature Selection (SLUFS). It is an ideal sparse subspace constraint which can
                                        overcome the drawbacks of the ℓ2,1-norm. However, optimizing the ℓ2,0-norm constraint is an NP-hard problem, and at
                                        present, only some approximate solutions can be given, but the convergence can not be guaranteed. To tackle this
                                        challenge, we design a novel alternative iterative algorithm to directly optimize the ℓ2,0-norm based model. Most
                                        importantly, our strategy can obtain a closed-form solution with strict convergence guarantee. Comprehensive experiments
                                        are conducted on several real-world datasets to evaluate the performance of SLUFS with comparison to several related
                                        state-of-the-art methods.}
                                        }

D. Hu, Z. Wang(通讯), F. Nie, R. Wang and X. Li, "Self-Supervised Learning for Heterogeneous Audiovisual Scene Analysis," IEEE Transactions on Multimedia, 2023. [BibTeX]|[PDF]

@ARTICLE{9743291,
                        author={Hu, Di and Wang, Zheng and Nie, Feiping and Wang, Rong and Li, Xuelong},
                        journal={IEEE Transactions on Multimedia},
                        title={Self-Supervised Learning for Heterogeneous Audiovisual Scene Analysis},
                        year={2023},
                        volume={25},
                        number={},
                        pages={3534-3545},
                        doi={10.1109/TMM.2022.3162477}}

F. Nie, Z. Wang(学一), R. Wang and X. Li, "Adaptive Local Embedding Learning for Semi-supervised Dimensionality Reduction," IEEE Transactions on Knowledge and Data Engineering, (CCF A), 2021. [BibTeX]|[PDF]

@ARTICLE{9314042,
                                        author={F. {Nie} and Z. {Wang} and R. {Wang} and X. {Li}},
                                        journal={IEEE Transactions on Knowledge and Data Engineering}, 
                                        title={Adaptive Local Embedding Learning for Semi-supervised Dimensionality Reduction}, 
                                        year={2021},
                                        volume={},
                                        number={},
                                        pages={1-1},
                                        doi={10.1109/TKDE.2021.3049371}}

F. Nie, Z. Wang(学一), T. Lai, R. Wang and X. Li, "Subspace Sparse Discriminative Feature Selection," IEEE Transactions on Cybernetics, 2020. [BibTeX]|[PDF]

@ARTICLE{9226125,
                                        author={F. {Nie} and Z. {Wang} and L. {Tian} and R. {Wang} and X. {Li}},
                                        journal={IEEE Transactions on Cybernetics}, 
                                        title={Subspace Sparse Discriminative Feature Selection}, 
                                        year={2020},
                                        volume={},
                                        number={},
                                        pages={1-13},
                                        doi={10.1109/TCYB.2020.3025205}}

Co-supervised with Prof. Nie:

H. Nie, Q. Li, Z. Wang(通讯), H. Zhao and F. Nie, "Semisupervised Subspace Learning With Adaptive Pairwise Graph Embedding," IEEE Transactions on Neural Networks and Learning Systems, in press, 2023. [BibTeX]|[PDF]

@ARTICLE{10253951,
                        author={Nie, Hebing and Li, Qi and Wang, Zheng and Zhao, Haifeng and Nie, Feiping},
                        journal={IEEE Transactions on Neural Networks and Learning Systems},
                        title={Semisupervised Subspace Learning With Adaptive Pairwise Graph Embedding},
                        year={2023},
                        volume={},
                        number={},
                        pages={1-15},
                        doi={10.1109/TNNLS.2023.3311789}}

S. Wang, F. Nie, Z. Wang, R. Wang and X. Li, "Outliers Robust Unsupervised Feature Selection for Structured Sparse Subspace," IEEE Transactions on Knowledge and Data Engineering, in press, 2023. [BibTeX]|[PDF]

@ARTICLE{10195216,
                        author={Wang, Sisi and Nie, Feiping and Wang, Zheng and Wang, Rong and Li, Xuelong},
                        journal={IEEE Transactions on Knowledge and Data Engineering},
                        title={Outliers Robust Unsupervised Feature Selection for Structured Sparse Subspace},
                        year={2023},
                        volume={},
                        number={},
                        pages={1-14},
                        doi={10.1109/TKDE.2023.3297226}}

S. Wang, F. Nie, Z. Wang, R. Wang and X. Li, "Sparse robust subspace learning via boolean weight," Information Fusion, 2023. [BibTeX]|[PDF]

@article{wang2023sparse,
                        title={Sparse robust subspace learning via boolean weight},
                        author={Wang, Sisi and Nie, Feiping and Wang, Zheng and Wang, Rong and Li, Xuelong},
                        journal={Information Fusion},
                        volume={96},
                        pages={224--236},
                        year={2023},
                        publisher={Elsevier}
                        }

Y. Yuan, Z. Wang(共一), F. Nie and X. Li, "Unsupervised Feature Selection with self-Weighted and L2, 0-Norm Constraint," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023. [BibTeX]|[PDF]

@inproceedings{yuan2023unsupervised,
                            title={Unsupervised Feature Selection with self-Weighted and ℓ 2, 0-Norm Constraint},
                            author={Yuan, Yongjin and Wang, Zheng and Nie, Feiping and Li, Xuelong},
                            booktitle={ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
                            pages={1--5},
                            year={2023},
                            organization={IEEE}
                            }

S. Wang, F. Nie, Z. Wang, R. Wang and X. Li, "Robust Principal Component Analysis via Joint Reconstruction and Projection," IEEE Transactions on Neural Networks and Learning Systems, in press, 2022. [BibTeX]|[PDF]

@ARTICLE{9946015,
                            author={Wang, Sisi and Nie, Feiping and Wang, Zheng and Wang, Rong and Li, Xuelong},
                            journal={IEEE Transactions on Neural Networks and Learning Systems},
                            title={Robust Principal Component Analysis via Joint Reconstruction and Projection},
                            year={2022},
                            volume={},
                            number={},
                            pages={1-15},
                            doi={10.1109/TNNLS.2022.3214307}}

F. Nie, C. Zhang, Z. Wang, R. Wang and X. Li, "Local Embedding Learning via Landmark-Based Dynamic Connections," IEEE Transactions on Neural Networks and Learning Systems, , 2023. [BibTeX]|[PDF]

@ARTICLE{9893739,
                            author={Nie, Feiping and Zhang, Canyu and Wang, Zheng and Wang, Rong and Li, Xuelong},
                            journal={IEEE Transactions on Neural Networks and Learning Systems},
                            title={Local Embedding Learning via Landmark-Based Dynamic Connections},
                            year={2023},
                            volume={34},
                            number={11},
                            pages={9481-9492},
                            doi={10.1109/TNNLS.2022.3203014}}

R. Wang, C. Zhang, J. Bian, Z. Wang, F. Nie and X. Li, "Sparse and Flexible Projections for Unsupervised Feature Selection," IEEE Transactions on Knowledge and Data Engineering, , 2023. [BibTeX]|[PDF]

@ARTICLE{9759987,
                            author={Wang, Rong and Zhang, Canyu and Bian, Jintang and Wang, Zheng and Nie, Feiping and Li, Xuelong},
                            journal={IEEE Transactions on Knowledge and Data Engineering},
                            title={Sparse and Flexible Projections for Unsupervised Feature Selection},
                            year={2023},
                            volume={35},
                            number={6},
                            pages={6362-6375},
                            doi={10.1109/TKDE.2022.3167996}}

F. Nie, S. Wang, Z. Wang, R. Wang and X. Li, "Discrete Robust Principal Component Analysis via Binary Weights Self-Learning," IEEE Transactions on Neural Networks and Learning Systems, , 2023. [BibTeX]|[PDF]

@ARTICLE{9749772,
                            author={Nie, Feiping and Wang, Sisi and Wang, Zheng and Wang, Rong and Li, Xuelong},
                            journal={IEEE Transactions on Neural Networks and Learning Systems},
                            title={Discrete Robust Principal Component Analysis via Binary Weights Self-Learning},
                            year={2023},
                            volume={34},
                            number={11},
                            pages={9064-9077},
                            doi={10.1109/TNNLS.2022.3155607}}

Co-authored:

Z. Zhao, R. Wang, Z. Wang, F. Nie and X. Li, "Graph Joint Representation Clustering via Penalized Graph Contrastive Learning," IEEE Transactions on Neural Networks and Learning Systems, , 2023. [BibTeX]|[PDF]

@ARTICLE{10237119,
                            author={Zhao, Zihua and Wang, Rong and Wang, Zheng and Nie, Feiping and Li, Xuelong},
                            journal={IEEE Transactions on Neural Networks and Learning Systems},
                            title={Graph Joint Representation Clustering via Penalized Graph Contrastive Learning},
                            year={2023},
                            volume={},
                            number={},
                            pages={1-12},
                            doi={10.1109/TNNLS.2023.3307149}}

Q. Wang, F. Wang, Z.Li, Z. Wang and F. Nie, "Coordinate Descent Optimized Trace Difference Model for Joint Clustering and Feature Extraction," Pattern Recognition, , 2023. [BibTeX]|[PDF]

@article{wang2023coordinate,
                                title={Coordinate Descent Optimized Trace Difference model for joint clustering and feature extraction},
                                author={Wang, Quan and Wang, Fei and Li, Zhongheng and Wang, Zheng and Nie, Feiping},
                                journal={Pattern Recognition},
                                pages={110062},
                                year={2023},
                                publisher={Elsevier}
                                }

Y. Guo, Y. Sun, Z. Wang, F. Nie and F. Wang, "Double-Structured Sparsity Guided Flexible Embedding Learning for Unsupervised Feature Selection," IEEE Transactions on Neural Networks and Learning Systems, , 2023. [BibTeX]|[PDF]

@ARTICLE{10123301,
                                author={Guo, Yu and Sun, Yuan and Wang, Zheng and Nie, Feiping and Wang, Fei},
                                journal={IEEE Transactions on Neural Networks and Learning Systems},
                                title={Double-Structured Sparsity Guided Flexible Embedding Learning for Unsupervised Feature Selection},
                                year={2023},
                                volume={},
                                number={},
                                pages={1-14},
                                doi={10.1109/TNNLS.2023.3267184}}

Z. Li, F. Nie, D. Wu, Z. Wang and X. Li, "Sparse Trace Ratio LDA for Supervised Feature Selection," IEEE Transactions on Cybernetics, ,in press, 2023. [BibTeX]|[PDF]

@ARTICLE{10113167,
                                author={Li, Zhengxin and Nie, Feiping and Wu, Danyang and Wang, Zheng and Li, Xuelong},
                                journal={IEEE Transactions on Cybernetics},
                                title={Sparse Trace Ratio LDA for Supervised Feature Selection},
                                year={2023},
                                volume={},
                                number={},
                                pages={1-14},
                                doi={10.1109/TCYB.2023.3264907}}

[更多文章]

学术活动

期刊、会议审稿人：IEEE Trans-NNLS、IEEE Trans-CYB、Pattern Recognition、AAAI、IJCAI、ICLR、NeurIPS等
2021获得中国博士后第70批面上资助
2021年获得IJCAI-SAIA青年精英学术会议"学术新星"奖
2022年获得陕西省自然科学基础研究计划青年项目资助

其他信息

欢迎报考，本人将亲自从模型构思、代码编程、文章撰写等方面进行实际指导，请有意向同学发送简历至zhengwangml AT gmail.com

下一篇：于登秀副教授

师资队伍