A new method of selecting safe neighbors for the Riemannian Manifold Learning algorithm

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dc.contributor.authorCarlini L. P.
dc.contributor.authorMiranda Junior G. F.
dc.contributor.authorGiraldi G. A.
dc.contributor.authorCarlos E. Thomaz
dc.contributor.authorOrcidhttps://orcid.org/0000-0001-5566-1963
dc.date.accessioned2022-01-12T21:54:27Z
dc.date.available2022-01-12T21:54:27Z
dc.date.issued2021-01-05
dc.description.abstract© 2003-2012 IEEE.Manifold learning (ML) comprehends a set of nonlinear techniques for mining and representing high-dimensional data. In this work, we approach the well-known and successful ML technique called Riemannian Manifold Learning (RML). Firstly, we present a geometric interpretation of the main steps of selecting visible and safe neighborhoods to reconstruct geometry and topology in the original RML algorithm. Then, we describe and implement a new method of selecting safe neighbors for this algorithm. Our experimental results on synthetic and real data sets, using open source tools and a public face image database, have showed that the new method proposed shows similar results to the original one and reconstructions that favour local rather than holistic similarities described by the data. Additionally, since the new method proposed requires the specification of only one input parameter, its implementation is simpler and more intuitive than the original one.
dc.description.firstpage89
dc.description.issuenumber1
dc.description.lastpage97
dc.description.volume19
dc.identifier.citationCARLINI, L. P; MIRANDA JUNIOR, G. P.; GIRALDI, G. A.; THOMAZ, C. E. A new method of selecting safe neighbors for the Riemannian Manifold Learning algorithm. IEEE Latin America Transactions, v. 19, n. 1, p. 89-97, Jan. 2021.
dc.identifier.doi10.1109/TLA.2021.9423851
dc.identifier.issn1548-0992
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/3601
dc.relation.ispartofIEEE Latin America Transactions
dc.rightsAcesso Restrito
dc.subject.otherlanguageManifold Learning
dc.subject.otherlanguageRML
dc.subject.otherlanguageSafe Neighbors
dc.titleA new method of selecting safe neighbors for the Riemannian Manifold Learning algorithm
dc.typeArtigo
fei.scopus.citations0
fei.scopus.eid2-s2.0-85105567007
fei.scopus.subjectFace image database
fei.scopus.subjectGeometric interpretation
fei.scopus.subjectHigh dimensional data
fei.scopus.subjectManifold learning
fei.scopus.subjectNonlinear techniques
fei.scopus.subjectOpen source tools
fei.scopus.subjectRiemannian manifold
fei.scopus.subjectSynthetic and real data
fei.scopus.updated2024-11-01
fei.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85105567007&origin=inward
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