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Volumn 2, Issue , 2005, Pages 1237-1240

Nonlinear feature extraction of hyperspectral data based on Locally Linear Embedding (LLE)

Author keywords

Dimensionality reduction; Feature extraction; Hyperspectral; Information content; Locally Linear Embedding; Principal Component Analysis

Indexed keywords

DIMENSIONALITY REDUCTION; HYPERSPECTRAL DATA; INFORMATION CONTENT; LOCALLY LINEAR EMBEDDING;

EID: 33745727401     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IGARSS.2005.1525342     Document Type: Conference Paper
Times cited : (51)

References (7)
  • 1
    • 77957741951 scopus 로고
    • On the mean accuracy of statistical pattern recognizers
    • G. F. Hughes, "On the Mean Accuracy of Statistical Pattern Recognizers," IEEE Trans. Inform. Theory, vol. IT-14, pp. 55-63, 1968.
    • (1968) IEEE Trans. Inform. Theory , vol.IT-14 , pp. 55-63
    • Hughes, G.F.1
  • 3
    • 0034704222 scopus 로고    scopus 로고
    • Nonlinear dimensionality reduction by locally linear embedding
    • Dec.
    • S. T. Roweis and L. K. Saul, "Nonlinear Dimensionality Reduction by Locally Linear Embedding," Science, vol. 290, Dec., 2000.
    • (2000) Science , vol.290
    • Roweis, S.T.1    Saul, L.K.2
  • 5
    • 2342517502 scopus 로고    scopus 로고
    • Think globally, fit locally: Unsupervised learning of low dimensional manifolds
    • L. K. S. a. S. T. Roweis, "Think globally, fit locally: Unsupervised Learning of Low Dimensional Manifolds," Journal of Machine Learning Research, pp. 119-155, 2003.
    • (2003) Journal of Machine Learning Research , pp. 119-155
    • Roweis, L.K.S.A.S.T.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.