图 书 馆
基于子空间学习的扰动格拉斯曼核Disturbance Grassmann Kernels for Subspace Based Learning |
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| 课程网址: | http://videolectures.net/kdd2018_hong_disturbance_grassmann/ |
| 主讲教师: | Junyuan Hong |
| 开课单位: | 中国科学技术大学 |
| 开课时间: | 2018-11-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In this paper, we focus on subspace-based learning problems, where data elements are linear subspaces instead of vectors. To handle this kind of data, Grassmann kernels were proposed to measure the space structure and used with classifiers, e.g., Support Vector Machines (SVMs). However, the existing discriminative algorithms mostly ignore the instability of subspaces, which would cause the classifiers to be misled by disturbed instances. Thus we propose considering all potential disturbances of subspaces in learning processes to obtain more robust classifiers. Firstly, we derive the dual optimization of linear classifiers with disturbances subject to a known distribution, resulting in a new kernel, Disturbance Grassmann (DG) kernel. Secondly, we research into two kinds of disturbance, relevant to the subspace matrix and singular values of bases, with which we extend the Projection kernel on Grassmann manifolds to two new kernels. Experiments on action data indicate that the proposed kernels perform better compared to state-of-the-art subspace-based methods, even in a worse environment. |
| 关 键 词: | 子空间的学习问题; 线性子空间; 空间矩阵和基的奇异值; 线性分类器的对偶优化 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-03-09：cyh |
| 最后编审: | 2023-05-15：cyh |
| 阅读次数: | 24 |