（1）获奖情况 [1]2022年入选云南省“兴滇英才支持计划”青年人才; [2]2017年中国电子学会科技进步奖三等奖一项（排名第五）； （2）教学/科研项目 [1]主持国家自然科学基金地区项目（62162033）“面向复杂多视图数据表示的深度矩阵/张量分解方法研究”; [2]主持国家自然科学基金青年项目（61603159）“面向低质量图像数据的稀疏低秩矩阵回归与分解方法研究”； [3]主持云南省重大科技专项课题（202402AD080001-3）“物理机理引导深度学习的工业设备故障辨识分析技术研究”； [4]主持云南省基础研究计划面上项目（202101AT070438）“面向大规模复杂跨模态数据的语义表示与检索方法研究”； [5]主持云南省科技厅-昆明理工大学“双一流”创建联合专项面上项目（202101BE070001-056）“基于稳健深度矩阵分解的肿瘤基因选择方法研究”; [6]主持2022年云南省“兴滇英才支持计划”青年人才项目“面向复杂大规模跨模态数据检索的哈希方法研究”; [7]主持江苏省自然科学基金青年项目（BK20160293）“基于稀疏低秩的鲁棒矩阵回归与分解方法研究”； [8]主持中国博士后科学基金项目(2017M611695)“基于稀疏低秩理论的图像回归与分解理论研究”； [9]主持江苏省博士后科学基金项目（1701094B）“面向高维图像鲁棒表示的稀疏低秩理论与方法研究”； [10]主持江苏省双创博士计划项目（科技副总类）； （3）论文 [1]Yibing Bai（硕士生),Zhenqiu Shu*, et al. Proxy-based graph convolutional hashing for cross-modal retrieval. IEEE Transaction on Big Data (中科院小类二区),2024: 10(4): 371 - 385. [2]Kailing Yong（硕士生),Zhenqiu Shu*, et al.Two-stage zero-shot sparse hashing with missing labels for cross-modal retrieval.Pattern Recognition (中科院大类一区TOP), 2024. [3]Li Li（硕士生),Zhenqiu Shu*, et al. Robust online hashing via label semantic enhancement for cross-modal retrieval. Pattern Recognition (中科院大类一区TOP), 2024, 145: 109972. [4]Kailing Yong（硕士生),Zhenqiu Shu*, et al.Zero-shot discrete hashing with adaptive class correlation for cross-modal retrieval, Knowledge-Based Systems (中科院大类一区TOP), 2024, 295:111820. [5] Kailing Yong（硕士生),Zhenqiu Shu*, et al. Unpaired robust hashing with noisy labels for zero-shot cross-modal retrieval. Engineering Applications of Artificial Intelligence (中科院小类一区), 2024, 133(Part B): 108197. [6] Bin Li (硕士生),Zhenqiu Shu*, et al. Multi-view clustering via label-embedded regularized NMF with dual-graph constraints. Neurocomputing(中科院大类二区), 2023: 551:126521. [7] Kailing Yong (硕士生),Zhenqiu Shu*, et al. Robust zero-shot discrete hashing with noisy labels for cross-modal retrieval. International Journal of Machine Learning and Cybernetics (中科院大类三区), 2024. [8]Zhenqiu Shu,etal.Robust Cross-modaldeephashing withrankinglearning fornoisylabels. IEEE Transaction on Big Data (中科院小类二区), 2024, Early Access Article. [9]Zhenqiu Shu, et al. Specific class center guided deep hashing for cross-modal retrieval. Information sciences(中科院大类一区TOP), 2022, 609: 304-318. [10]Zhenqiu Shu, et al. Incomplete multi-view clustering based on alignment fusion of view interactive attention information. Expert Systems With Applications(中科院大类一区),2024, 252(B): 124258. [11]Zhenqiu Shu,et al. Dualattention Transformernetwork forhyperspectralimageclassification. Engineering Applications of Artificial Intelligence (中科院小类一区), 2024, 127(Part B): 107351. [12]Zhenqiu Shu, et al. Online supervised collective matrix factorization hashing for cross-modal retrieval. Applied Intelligence(中科院大类二区), 2023, 53(11): 14201-14218. [13]Zhenqiu Shu, et al. Spatial-spectral split attention residual network for hyperspectral image classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (中科院大类二区), 2023, 16:419-430. [14]Zhenqiu Shu,et al. Discrete asymmetric zero-shot hashing with application to cross-modal retrieval. Neurocomputing (中科院大类二区), 2022, 511: 366-379. [15]Zhenqiu Shu, et al. Dual local learning regularized NMF with sparse and orthogonal constraints. Applied Intelligence(中科院大类二区), 2023: 53(7): 7713-7727. [16]Zhenqiu Shu, et al. Robust graph regularized NMF with dissimilarity and similarity constraints for scRNA-seq data clustering. Journal of Chemical Information and Modeling (中科院大类二区), 2022, 62(23): 6271-6286. [17]Zhenqiu Shu, et al. Structure-guided feature and cluster contrastive learning for multi-view clustering. Neurocomputing (中科院大类二区), 2024，582: 127555. [18]Zhenqiu Shu, et al. Correntropy-based dual graph regularized nonnegative matrix factorization with Lp smoothness for data representation. Applied Intelligence (中科院大类二区), 2022, 52(7): 7653-7669. [19]Zhenqiu Shu, et al. Robust supervised matrix factorization hashing with application to cross-modal retrieval. Neural Computing and Applications (中科院大类三区), 2023, 35(9): 6665-6684. [20]Zhenqiu Shu,et al. Robust dual-graph regularized deep matrix factorization for multi-view clustering. Neural Processing Letters (中科院大类四区), 2023, 55(5): 6067-6087. [21]Zhenqiu Shu, et al. Dual local learning regularized non-negative matrix factorization and its semi-supervised extension for clustering. Neural Computing and Applications (中科院大类二区), 2021, 33(11): 6213-6231. [22]Zhenqiu Shu, et al. Rank-constrained nonnegative matrix factorization algorithm for data representation. Information Sciences (中科院大类一区Top期刊), 2020, 528: 133-146. [23]Zhenqiu Shu,et al. Parameter-less auto-weighted multiple graphs regularized nonnegative matrix factorization for data representation. Knowledge-based Systems (中科院大类二区), 2017,131:105-112. [24]Zhenqiu Shu, et al. Local regularization concept factorization and its semi-supervised extension for image representation. Neurocomputing (中科院大类二区), 2015, 152(22):1-12. [25]Zhenqiu Shu, et al. Local and global regularized sparse coding for data representation. Neurocomputing (中科院大类二区), 2016, 198(29): 188-197. （4）知识产权 [1]舒振球等.无参数自动加权多图正则化非负矩阵分解及图像识别方法.发明专利，授权号：CN107609596, 2020.（已授权） [2]舒振球等.封顶概念分解方法及图像聚类方法.发明专利，申请号：201711257431.6，2017.（已授权） [3]舒振球等.面向多视图聚类的多图正则化深度矩阵分解方法.发明专利，申请号：20180607971.0, 2018.（已授权） [4]舒振球等.基于局部学习正则化的深度矩阵分解方法及图像聚类方法，发明专利，申请号：201810905948.X，2018.（已授权） [5]舒振球等.一种稀疏对偶约束的高光谱图像解混方法,发明专利，专利号：ZL 201910514472.1, 2023（已授权） [6]舒振球等.基于深度矩阵的约束概念分解聚类方法，发明专利，专利号: 201811281896X, 2023（已授权） [7]舒振球等.基于对偶局部学习的非负矩阵分解聚类方法.发明专利，专利号: 2018112216734, 2023（已授权） [8]舒振球等.一种基于对偶局部一致的约束稀疏概念分解的聚类方法，专利号：2020105078760, 2023(已授权) [9]舒振球等.一种基于相似性零样本哈希的跨模态检索方法，专利号：ZL202210696434.4, 2024(已授权) 专著、教材 |
