Dimensionality Reduction on Grassmannian: A Good Practice

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 光电信息技术研究室

	Dimensionality Reduction on Grassmannian: A Good Practice
	Li CX(李晨曦)1,2,3 ; Liu YP(刘云鹏)1,2 ; Shi ZL(史泽林)1,2 ; Liu TC(刘天赐)1,2,3
	2017
会议日期	December 29-31, 2017
会议地点	Harbin, China
关键词	Grassmann manifold Riemannian optimization image-set recognition dimensionality reduction
页码	87-92
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	Proceedings of 2017 International Conference on Intelligent Computing and Information Systems (ICIS 2017)
会议录出版者	IEEE
会议录出版地	New York
语种	英语
ISBN号	978-1-5386-0842-5
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/23787]
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.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 2.Key Laboratory of Optical-Electronics Information Processing 3.University of Chinese Academy of Sciences, Beijing 100049, China
推荐引用方式 GB/T 7714	Li CX,Liu YP,Shi ZL,et al. Dimensionality Reduction on Grassmannian: A Good Practice[C]. 见:. Harbin, China. December 29-31, 2017.