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Volumn 4, Issue , 2006, Pages 194-197

Building connected neighborhood graphs for locally linear embedding

Author keywords

Dimensionality reduction; Locally linear embedding; Manifold learning

Indexed keywords

DATA REDUCTION; DATA STRUCTURES; LEARNING SYSTEMS;

EID: 34147146900     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPR.2006.345     Document Type: Conference Paper
Times cited : (12)

References (10)
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    • Think globally, fit locally: Unsupervised learning of low dimensional manifolds
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    • L. K. Saul and S. T. Roweis. Think globally, fit locally: Unsupervised learning of low dimensional manifolds. J. Machine Learning Research, 4:119-155, June 2003.
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    • Saul, L.K.1    Roweis, S.T.2
  • 6
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    • A global geometric framework for nonlinear dimensionality reduction
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    • J. B. Tenenbaum, V. de Silva, and J. C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290:2319-2323, Dec. 2000.
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    • Tenenbaum, J.B.1    de Silva, V.2    Langford, J.C.3
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    • L. Yang, k-edge connected neighborhood graph for geodesic distance estimation and nonlinear data projection. In Proc. 17th Inter. Conf. Pattern Recognition (ICPR'04), 1, pages 196-199, Cambridge, UK, Aug. 2004.
    • L. Yang, k-edge connected neighborhood graph for geodesic distance estimation and nonlinear data projection. In Proc. 17th Inter. Conf. Pattern Recognition (ICPR'04), volume 1, pages 196-199, Cambridge, UK, Aug. 2004.
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    • Building k-edge-connected neighborhood graphs for distance-based data projection
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    • L. Yang. Building k-edge-connected neighborhood graphs for distance-based data projection. Pattern Recognition Letters, 26(13):2015-2021, Oct. 2005.
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    • Yang, L.1
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    • Building k edge-disjoint spanning trees of minimum total length for isometric data embedding
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    • L. Yang. Building k edge-disjoint spanning trees of minimum total length for isometric data embedding. IEEE Trans. Pattern Analysis and Machine Intelligence, 27(10): 1680-1683, Oct. 2005.
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    • Building k-connected neighborhood graphs for isometric data embedding
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    • L. Yang. Building k-connected neighborhood graphs for isometric data embedding. IEEE Trans. Pattern Analysis and Machine Intelligence, 28(5):827-831, May 2006.
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    • Yang, L.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.