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本人是国家级青年人才项目入选者(2024年),并且获批国自然面上项目和省基金联合引导项目。本人的研究方向聚焦于新一代人工智能技术的前沿领域,主要研究基于低秩模型的高效处理高维数据的方法,致力于解决大数据产业中常见的数据结构复杂、带强噪声、有损毁或缺失等问题,以此克服传统机器学习方法失效的局限性。在多年的研究过程中,本人采用基于低秩三维数组的非凸优化方法,成功攻克了多视数据子空间聚类、高维数据的补全、...
杨明
Professor
Paper Publications
Tensorized Tri-factor Decomposition for Multi-view Clustering,IEEE Transactions on Circuits and Systems for Video Technology,35(6):5355-5366
Non-convex tensorial multi-view clustering by integrating 1-based sliced-Laplacian regularization and 2,p-sparsity,Pattern Recognition,154()
Tensorized Soft Label Learning Based on Orthogonal NMF,IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,36(7):13219-13231
Non-convex tensorial multi-view clustering by integrating l1-based sliced-Laplacian regularization and l2,p-sparsity,PATTERN RECOGNITION,154()
Nonconvex low-rank regularization method for video snapshot compressive imaging,Applied Mathematical Modelling,137()
Non-convex tensorial multi-view clustering by integrating ℓ1-based sliced-Laplacian regularization and ℓ2,p-sparsity,Pattern Recognition,154()
Efficient Multi-View -Means for Image Clustering,IEEE Transactions on Image Processing,33():273-284
Orthogonal Non-negative Tensor Factorization based Multi-view Clustering,NEURAL INFORMATION PROCESSING SYSTEMS (NIPS),36.0()
Multi-source partial multi-label learning via tensor decomposition and nonconvex regularization,INFORMATION FUSION,112()
Semi-supervised sparse representation collaborative clustering of incomplete data,APPLIED INTELLIGENCE,53(24):31065-31076
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