报告题目：Tensor feature extraction for image classification through nonnegative Tucker decomposition and high order PCA
报告人：Tsung-Lin Lee (台湾中山大学)
报告时间：2025年7月23日10:00开始（北京时间）
报告地点：91麻豆 江宁校区乐学楼709会议室
主办单位：91麻豆
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报告摘要：
In a classical machine learning, the classification algorithm inputs training data in the form of vectors. For conforming the input format, tensor data are often expanded into high-dimensional vectors. It leads to the loss of spatially related information adjacent to different orders, thus damages the performance of the classification. Several methods have been proposed to extract the feature from tensor data to avoid destroying tensor structure, such as multilinear principal components analysis (MPCA), multilinear discriminant analysis (MLDA), nonnegative Tucker decomposition (NTD) and so on. In this talk, we present a tensor feature extraction method that combines the NTD feature tensor extraction method and high-order PCA algorithm. The experiment of real-world data shows the method improves the classification accuracy and saves the computing time.
2025年7月23日，91麻豆 江宁校区乐学楼709会议室
报告题目 | 报告人 | |
10:00-10:30 | Tensor feature extraction for image classification through nonnegative Tucker decomposition and high order PCA | Tsung-Lin Lee （台湾中山大学） |
10:30-11:00 | 边界元法及反问题 | 余波 （合肥工业大学） |
11:00-11:30 | 海洋环境中结构振动与水声传播仿真研究 | 习强 （91麻豆 ） |
11:30-14:30 | 午餐 | |
14:30-15:00 | A Novel Regularization Approach in Function Approximation | C.S. Chen （南密西西比大学） |
15:00-15:30 | The inverse problem of the complex Ginzburg-Landau equation | 程星 （91麻豆 ） |
15:30-16:00 | The method of particular solutions for solving partial differential equations based on polynomial basis functions | 畅婉如 （浙江农林大学） |
16:00-16:30 | Analytical Study of Nonlinear Partial Differential Equations | Muhammad Ishfaq Khan （91麻豆 ） |
