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Volumn 7496, Issue , 2009, Pages

Local graph cut criterion for supervised dimensionality reduction

Author keywords

Graph cut criterion; k nearest neighbor graph; Supervised dimensionality reduction

Indexed keywords

CLUSTERING PROBLEMS; DIMENSIONALITY REDUCTION; GENERALIZED EIGENVALUE PROBLEMS; GRAPH CUT; HETEROSCEDASTIC; K-NEAREST NEIGHBOR GRAPHS; MULTI-MODEL; NORMALIZED CUTS; PROJECTION MATRIX;

EID: 71649105441     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.832411     Document Type: Conference Paper
Times cited : (7)

References (14)
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  • 3
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  • 4
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  • 5
    • 70349192857 scopus 로고    scopus 로고
    • An information geometric approach to supervised dimensionality reduction
    • Carter, K.M., Raich, R. and Hero, A.O., "An information geometric approach to supervised dimensionality reduction," IEEE Con. on ICASSP, 1829 - 1832(2009).
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    • Carter, K.M.1    Raich, R.2    Hero, A.O.3
  • 6
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  • 9
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    • Normalized cuts and image segmentation
    • Shi, J. and Malik, J., "Normalized cuts and image segmentation," IEEE Tran. on PAMI, 22(8), 888-905(2000).
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  • 10
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    • A min-max cut algorithm for graph partitioning and data clustering
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    • Optimal dimensionality discriminant analysis and its application to image recognition
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    • Local fisher discriminant analysis for supervised dimensionality reduction
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.