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Volumn 16, Issue 8, 2005, Pages 1423-1430

Study on the low-dimensional embedding and the embedding dimensionality of manifold of high-dimensional data

Author keywords

Embedding dimensionality; Isomap; Isometric mapping; Toroidal manifold

Indexed keywords

COMPUTER SIMULATION; IMAGE CODING; OPTICAL FLOWS;

EID: 24044550981     PISSN: 10009825     EISSN: None     Source Type: Journal    
DOI: 10.1360/jos161423     Document Type: Article
Times cited : (21)

References (12)
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    • When does ISOMAP recover the natural parameterization of families of articulated images
    • Technical Report, 2002-27, Department of Statistics, Stanford University
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    • Hessian eigenmaps: New locally linear embedding techniques for high-dimensional data
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    • Grouping and dimensionality reduction by locally linear embedding
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    • Determining the dimensionality of multidimensional scaling models for cognitive modeling
    • Lee MD. Determining the dimensionality of multidimensional scaling models for cognitive modeling. Journal of Mathematical Psychology, 2001,45(4):149-166.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.