Zheng Wang, Assistant Professor-光电与智能研究院(iOPEN)—

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Zheng Wang, Assistant Professor

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Zheng Wang, Assistant Professor

Scholar Homepage：[Google Scholar]

Research Direction：His current research interests include machine learning, deep audiovisual learning and intelligent video analysis etc.

Contact Address：127 West Youyi Road, Beilin District, Xi'an 710072, Shaanxi, P.R. China

Zip Code：710072

E-mail：zhengwangml AT gmail.com

Education Background

2017-2021	Northwestern Polytechnical University	Computer Science and Technology	PhD
2014-2017	Anhui University	Computer Science and Technology	Master
2010-2014	Anhui Juanzhu University	Information and Computing Science	Bachelor

Representative Papers

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}
                          }

F. Nie, Z. Wang, 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), in press, 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}
                          }

F. Nie, Z. Wang, T. Lai, R. Wang and X. Li, “Subspace Sparse Discriminative Feature Selection,” IEEE Transactions on Cybernetics, (IF: 11.079) in press, 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}}

F. Nie, Z. Wang, R. Wang and X. Li, “Submanifold-preserving discriminant analysis with an auto-optimized graph,” IEEE Transactions on Cybernetics, (IF: 11.079) in press, 2019. [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}}

F. Nie, Z. Wang, R. Wang and X. Li, “Adaptive Local Embedding Learning for Semi-supervised Dimensionality Reduction,” IEEE Transactions on Knowledge and Data Engineering, (CCF A) , in press, 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}}

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