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Kernel sparse representation on Grassmann manifolds for visual clustering
Liu TC(刘天赐)1,2; Shi ZL(史泽林)1,3; Liu YP(刘云鹏)1,3
Department光电信息技术研究室
Source PublicationOptical Engineering
ISSN0091-3286
2018
Volume57Issue:5Pages:1-10
Indexed BySCI ; EI
EI Accession number20182105218002
WOS IDWOS:000435435300013
Contribution Rank1
Funding OrganizationNational Natural Science Foundation of China ; Common Technical Project of Equipment Development Department
KeywordGrassmann Manifold Visual Clustering Sparse Representation Kernel Method
Abstract

Image sets and videos can be modeled as subspaces, which are actually points on Grassmann manifolds. Clustering of such visual data lying on Grassmann manifolds is a hard issue based on the fact that the state-of-The-Art methods are only applied to vector space instead of non-Euclidean geometry. Although there exist some clustering methods for manifolds, the desirable method for clustering on Grassmann manifolds is lacking. We propose an algorithm termed as kernel sparse subspace clustering on the Grassmann manifold, which embeds the Grassmann manifold into a reproducing kernel Hilbert space by an appropriate Gaussian projection kernel. This kernel is applied to obtain kernel sparse representations of data on Grassmann manifolds utilizing the self-expressive property and exploiting the intrinsic Riemannian geometry within data. Although the Grassmann manifold is compact, the geodesic distances between Grassmann points are well measured by kernel sparse representations based on linear reconstruction. With the kernel sparse representations, clustering results of experiments on three prevalent public datasets outperform a number of existing algorithms and the robustness of our algorithm is demonstrated as well.

Language英语
WOS SubjectOptics
WOS KeywordCLASSIFICATION ; SUBSPACES
WOS Research AreaOptics
Funding ProjectNational Natural Science Foundation of China[61540069] ; Common Technical Project of Equipment Development Department[Y6K4250401]
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/21875
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, China
2.University of Chinese Academy of Sciences, Beijing, China
3.Key Laboratory of Opto-Electronic Information Processing, Shenyang, China
Recommended Citation
GB/T 7714
Liu TC,Shi ZL,Liu YP. Kernel sparse representation on Grassmann manifolds for visual clustering[J]. Optical Engineering,2018,57(5):1-10.
APA Liu TC,Shi ZL,&Liu YP.(2018).Kernel sparse representation on Grassmann manifolds for visual clustering.Optical Engineering,57(5),1-10.
MLA Liu TC,et al."Kernel sparse representation on Grassmann manifolds for visual clustering".Optical Engineering 57.5(2018):1-10.
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