Efficient isometric multi-manifold learning based on the self-organizing method
Fan, Mingyu1; Zhang, Xiaoqin1; Qiao, Hong2,3; Zhang, Bo4,5
Source PublicationINFORMATION SCIENCES
2016-06-01
Volume345Pages:325-339
SubtypeArticle
AbstractGeodesic distance, as an essential measurement for data similarity, has been successfully used in manifold learning. However, many geodesic based isometric manifold learning algorithms, such as the isometric feature mapping (Isomap) and GeoNLM, fail to work on data that distribute on clusters or multiple manifolds. This limits their applications because practical data sets generally distribute on multiple manifolds. In this paper, we propose a new isometric multi-manifold learning method called Multi-manifold Proximity Embedding (MPE) which can be efficiently optimized using the gradient descent method or the self-organizing method. Compared with the previous methods, the proposed method has two steps which can isometrically learn data distributed on several manifolds and is more accurate in preserving both the intra-manifold and the inter-manifold geodesic distances. The effectiveness of the proposed method in recovering the nonlinear data structure and clustering is demonstrated through experiments on both synthetically and real data sets. (C) 2016 Elsevier Inc. All rights reserved.
KeywordIsomap Nonlinear Dimensionality Reduction Manifold Learning Pattern Analysis Multi-manifold Embedding
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.ins.2016.01.069
WOS KeywordNONLINEAR DIMENSIONALITY REDUCTION ; CONNECTED NEIGHBORHOOD GRAPHS ; FEATURE-EXTRACTION ; FACE RECOGNITION ; ALGORITHM ; FRAMEWORK ; DISTANCE
Indexed BySCI
Language英语
Funding OrganizationNNSF of China Grant(61203241 ; NSF of Zhejiang Province(LY15F030011) ; Strategic Priority Research Program of the CAS(XDB02080003) ; NCMIS ; 61473212 ; 61472285 ; 6141101224 ; 61305035 ; 61379093 ; 11131006)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems
WOS IDWOS:000372687300022
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11375
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Affiliation1.Wenzhou Univ, Coll Math & Informat Sci, Wenzhou 325035, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management Control Complex Syst, Beijing 100190, Peoples R China
3.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China
4.Chinese Acad Sci, LSEC, Beijing 100190, Peoples R China
5.Chinese Acad Sci, AMSS, Inst Appl Math, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Fan, Mingyu,Zhang, Xiaoqin,Qiao, Hong,et al. Efficient isometric multi-manifold learning based on the self-organizing method[J]. INFORMATION SCIENCES,2016,345:325-339.
APA Fan, Mingyu,Zhang, Xiaoqin,Qiao, Hong,&Zhang, Bo.(2016).Efficient isometric multi-manifold learning based on the self-organizing method.INFORMATION SCIENCES,345,325-339.
MLA Fan, Mingyu,et al."Efficient isometric multi-manifold learning based on the self-organizing method".INFORMATION SCIENCES 345(2016):325-339.
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