SIA OpenIR  > 光电信息技术研究室
Dimensionality Reduction on Grassmannian: A Good Practice
Liu TC(刘天赐)1,2,3; Shi ZL(史泽林)1,3; Liu YP(刘云鹏)1,3; Li CX(李晨曦)1,2,3
Department光电信息技术研究室
Conference Name2017 International Conference on Intelligent Computing and Information Systems
Conference DateDecember 29-31, 2017
Conference PlaceHarbin, China
Source PublicationProceedings of 2017 International Conference on Intelligent Computing and Information Systems (ICIS 2017)
PublisherIEEE
Publication PlaceNew York
2017
Pages87-92
Contribution Rank1
ISBN978-1-5386-0842-5
KeywordGrassmann manifold Riemannian optimization image-set recognition dimensionality reduction
AbstractRepresenting 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.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/23787
Collection光电信息技术研究室
Corresponding AuthorLiu TC(刘天赐)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
3.Key Laboratory of Optical-Electronics Information Processing
Recommended Citation
GB/T 7714
Liu TC,Shi ZL,Liu YP,et al. Dimensionality Reduction on Grassmannian: A Good Practice[C]. New York:IEEE,2017:87-92.
Files in This Item:
File Name/Size DocType Version Access License
Dimensionality Reduc(708KB)会议论文 开放获取CC BY-NC-SAView Application Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Liu TC(刘天赐)]'s Articles
[Shi ZL(史泽林)]'s Articles
[Liu YP(刘云鹏)]'s Articles
Baidu academic
Similar articles in Baidu academic
[Liu TC(刘天赐)]'s Articles
[Shi ZL(史泽林)]'s Articles
[Liu YP(刘云鹏)]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Liu TC(刘天赐)]'s Articles
[Shi ZL(史泽林)]'s Articles
[Liu YP(刘云鹏)]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Dimensionality Reduction on Grassmannian A Good Practice.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.