CSpace
Curvature-aware manifold learning
Li, Yangyang1,2
2018-11-01
Source PublicationPATTERN RECOGNITION
ISSN0031-3203
Volume83Pages:273-286
AbstractOne of the fundamental assumptions of traditional manifold learning algorithms is that the embedded manifold is globally or locally isometric to Euclidean space. Under this assumption, these algorithms divided manifold into a set of overlapping local patches which are locally isometric to linear subsets of Euclidean space. Then the learnt manifold would be a flat manifold with zero Riemannian curvature. But in the general cases, manifolds may not have this property. To be more specific, the traditional manifold learning does not consider the curvature information of the embedded manifold. In order to improve the existing algorithms, we propose a curvature-aware manifold learning algorithm called CAML. Without considering the local and global assumptions, we will add the curvature information to the process of manifold learning, and try to find a way to reduce the redundant dimensions of the general manifolds which are not isometric to Euclidean space. The experiments have shown that CAML has its own advantage comparing to other traditional manifold learning algorithms in the sense of the neighborhood preserving ratios (NPR) on synthetic databases and classification accuracies on image set classification. (C) 2018 Elsevier Ltd. All rights reserved.
KeywordManifold learning Riemannian curvature Second fundamental form Hessian operator
DOI10.1016/j.patcog.2018.06.007
Language英语
Funding ProjectNational Key Research and Development Program of China[2016YFB1000902] ; NSFC[61232015] ; NSFC[61472412] ; NSFC[61621003] ; Beijing Science and Technology Project: Machine Learning based Stomatology; Tsinghua-Tencent-AMSS-Joint Project: WWW Knowledge Structure and its Application
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000442172200021
PublisherELSEVIER SCI LTD
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/31084
Collection中国科学院数学与系统科学研究院
Affiliation1.Chinese Acad Sci, Acad Math & Syst Sci, Key Lab MADIS, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Li, Yangyang. Curvature-aware manifold learning[J]. PATTERN RECOGNITION,2018,83:273-286.
APA Li, Yangyang.(2018).Curvature-aware manifold learning.PATTERN RECOGNITION,83,273-286.
MLA Li, Yangyang."Curvature-aware manifold learning".PATTERN RECOGNITION 83(2018):273-286.
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