Dimensionality reduction on grassmannian: A good practice
文献类型：会议论文
| 作者 | Liu TC(刘天赐)1,2,3 ; Shi ZL(史泽林)2,3; Liu YP(刘云鹏)2,3; Li CX(李晨曦)1,2,3
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| 出版日期 | 2018 |
| 会议日期 | July 19-21, 2018 |
| 会议地点 | Harbin, China |
| 关键词 | Grassmann manifold Riemannian optimization image-set recognition dimensionality reduction |
| 页码 | 943-948 |
| 英文摘要 | Representing images and videos as linear subspaces for visual recognition has made a great success which benefits from the Riemannian geometry named the Grassmann manifold. However, subspaces in vision are high-dimensional, which leads to a high computational expense and limited applicability of existing techniques. In this paper, we propose a generalized model to learn a lower-dimensional and more discriminative Grassmann manifold from the high dimensional one through an orthonormal projection for a better classification. We respect the Riemannian geometry of the Grassmann manifold and search for this projection directly from one Grassmann manifold to another face-to-face without any additional transformations. In this natural geometry-aware way, any metric on the Grassmann manifold can be resided in our model theoretically. We have combined different metrics with our model and the learning process can be treated as an unconstrained optimization problem on a Grassmann manifold. Experiments on several action datasets demonstrate that our approach can improve a more favorable accuracy over the state-of-the-art algorithms. |
| 产权排序 | 1 |
| 会议录 | 2018 Eighth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC 2018)
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| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-5386-8246-3 |
| 源URL | [http://ir.sia.cn/handle/173321/26726]  |
| 专题 | 沈阳自动化研究所_光电信息技术研究室 |
| 通讯作者 | Liu TC(刘天赐) |
| 作者单位 | 1.University of Chinese Academy of Sciences, Beijing 100049, China 2.Key Laboratory of Optical-Electronics Information Processing, China 3.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China |
| 推荐引用方式 GB/T 7714 | Liu TC,Shi ZL,Liu YP,et al. Dimensionality reduction on grassmannian: A good practice[C]. 见:. Harbin, China. July 19-21, 2018. |
入库方式： OAI收割
来源：沈阳自动化研究所
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