制服诱惑

[1] Xijiong Xie, Yujie Xiong, Generalized multi-view learning based on generalized eigenvalues proximal support vector machines, Expert Systems With Applications, vol.194, pp.116491, 2022

[2] Xijiong Xie, Shiliang Sun, Multi-view support vector machines with the consensus and complementarity information, IEEE Transactions on Knowledge and Data Engineering, vol.32, pp.2401-2413, 2020

[3] Chunling Lou, Xijiong Xie, Multi-view universum support vector machines with insensitive pinball loss, Expert Systems with Applications, vol.248, pp.123480, 2024

[4] Xijiong Xie, Yanfeng Li, Shiliang Sun, Deep multi-view multiclass twin support vector machines, Information Fusion, vol.91, pp.80-92, 2023

[5] Juqi Lu, Xijiong Xie, Yujie Xiong, Multi-view hypergraph regularized Lp norm least squares twin support vector machines for semi-supervised learning, Pattern Recognition, vol.156, pp.110753, 2023

[6] Xijiong Xie, Shiliang Sun, General multi-view semi-supervised least squares support vector machines with multi-manifold regularization, Information Fusion, vol.62, pp.63-72, 2020

[7] Xijiong Xie, Feixiang Sun, Jiangbo Qian, Lijun Guo, Rong Zhang, Xulun Ye, Zhijin Wang，Laplacian Lp norm least squares twin support vector machine. Pattern Recognition, vol.136, pp.109192, 2023

[8] Zhiwen Cao, Xijiong Xie, Yuqi Li, Multi-view unsupervised feature selection with consensus partition and diverse graph, Information Sciences, vol. 661, pp.120178, 2024

[9] Zhiwen Cao, Xijiong Xie, Partition-Level Tensor Learning-Based Multiview Unsupervised Feature Selection, IEEE Transactions on Neural Networks and Learning Systems, pp.1-13, DOI: 10.1109/TNNLS.2024.3482440, 2024

[10]  Shengke Xu, Xijiong Xie, Zhiwen Cao, Graph-Regularized consensus learning and diversity representation for unsupervised multi-view feature selection, Knowledge-Based Systems, vol.311, pp.113043, 2025

[1] 国家自然科学基金/青年项目，一般多视图核机的监督和半监督学习方法的研究，61906101 ，2020.1-2022.12，主持；

[2] 浙江省自然科学基金/青年项目，多视角迁移特征提取和分类方法的研究，LQ18F020001，2018.1-2020.12，主持；

[3] 浙江大学CAD&CG国家重点实验室开放课题，基于双支持向量机的域适应方法的研究，A1815， 2018.1-2018.12，主持；

[4]  宁波市自然科学基金/一般项目，基于深度学习的多视图支持向量机及其抗噪方法的研究，2023J115，2023.6-2026.5,主持；

[5]  宁波市自然科学基金/青年项目，基于双平面支持向量机的域适应方法研究，2018A610155，2018.11-2020.10,主持;

[6]  浙江省教育厅项目，双平面支持向量机的预适应方法 ，Y201635608，2016.11-2018.10,主持;

1. 2020年，上海市，上海市自然科学技术二等奖。

2. 2023年，中国自动化学会，中国自动化学会自然科学技术一等奖。