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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 Name2018 Eighth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC 2018)
Conference DateJuly 19-21, 2018
Conference PlaceHarbin, China
Source Publication2018 Eighth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC 2018)
PublisherIEEE
Publication PlaceNew York
2018
Pages943-948
Indexed ByEI
EI Accession number20201708511072
Contribution Rank1
ISBN978-1-5386-8246-3
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/173321/26726
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, China
Recommended Citation
GB/T 7714
Liu TC,Shi ZL,Liu YP,et al. Dimensionality reduction on grassmannian: A good practice[C]. New York:IEEE,2018:943-948.
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