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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
Discriminative attention based weighted sparse representation of visual objects in complex scenarios,Pattern Recognition,172()
Self-Supervised Graph Embedding Clustering,IEEE Transactions on Pattern Analysis and Machine Intelligence,47(12):11662-11673
Interpretable multi-view clustering via anchor graph-based tensor decomposition with convergence guarantees,Pattern Recognition,171()
Multi-view K-means clustering via manifold learning and tensor nonconvex regularization,APPLIED MATHEMATICAL MODELLING,149()
Large-scale stochastic sparse subspace representation with consensus anchor guidance,APPLIED INTELLIGENCE,55(7)
Class label fusion guided correlation learning for incomplete multi-label classification,Information Fusion,120()
Image Clustering With Transition Probabilities Learning,IEEE Transactions on Image Processing,34():1441-1453
Nonconvex tensorial submodule clustering of 2-D images by mining local and global structural information,APPLIED MATHEMATICAL MODELLING,143()
Embedded Multi-View Clustering via Collaborative Tensor Subspace Representation and Multi-Graph Fusion,IEEE SIGNAL PROCESSING LETTERS,32():911-915
Multi-view clustering based on feature selection and semi-non-negative anchor graph factorization,Neural Networks,184()
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