师资队伍

Representative works (last three years):

1. 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}}

2. 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} }

3. 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}}

4. 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}}

5. 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}}

6. 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}}

7. 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} }

8. 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.} }

9. 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}}

10. 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}}

11. 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}}