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Volumn 5748 LNCS, Issue , 2009, Pages 272-281

Making archetypal analysis practical

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION COSTS; CONVEX COMBINATIONS; DIMENSIONALITY REDUCTION; EXTREMAL; LARGE SCALE DATA; MULTIVARIATE DATA; NUMBER OF DATUM; ORIGINAL ALGORITHMS;

EID: 70350495789     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-03798-6_28     Document Type: Conference Paper
Times cited : (60)

References (16)
  • 1
    • 0028532769 scopus 로고
    • Archetypal Analysis
    • Cutler, A., Breiman, L.: Archetypal Analysis. Technometrics 36(4), 338-347 (1994)
    • (1994) Technometrics , vol.36 , Issue.4 , pp. 338-347
    • Cutler, A.1    Breiman, L.2
  • 3
    • 0347243182 scopus 로고    scopus 로고
    • Nonlinear Component Analysis as a Kernel Eigenvalue Problem
    • Schölkopf, B., Smola, A.J., Müller, K.-R.: Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation 10(5), 1299-1319 (1998)
    • (1998) Neural Computation , vol.10 , Issue.5 , pp. 1299-1319
    • Schölkopf, B.1    Smola, A.J.2    Müller, K.-R.3
  • 4
    • 0033592606 scopus 로고    scopus 로고
    • Learning the Parts of Objects by Non-Negative Matrix Factorization
    • Lee, D.D., Seung, S.: Learning the Parts of Objects by Non-Negative Matrix Factorization. Nature 401(6755), 788 (1999)
    • (1999) Nature , vol.401 , Issue.6755 , pp. 788
    • Lee, D.D.1    Seung, S.2
  • 6
    • 0000876180 scopus 로고    scopus 로고
    • Archetypal Analysis of Spatio-temporal Dynamics
    • Stone, E., Cutler, A.: Archetypal Analysis of Spatio-temporal Dynamics. Physica D 90(3), 209-224 (1996)
    • (1996) Physica D , vol.90 , Issue.3 , pp. 209-224
    • Stone, E.1    Cutler, A.2
  • 9
    • 0002714543 scopus 로고    scopus 로고
    • Making Large-Scale Support Vector Machine Learningn Practical
    • MIT Press, Cambridge
    • Joachims, T.: Making Large-Scale Support Vector Machine Learningn Practical. In: Advances in Kernel Methods: Support Vector Learning, MIT Press, Cambridge (1999)
    • (1999) Advances in Kernel Methods: Support Vector Learning
    • Joachims, T.1
  • 12
    • 22144471532 scopus 로고    scopus 로고
    • Neighborliness of Randomly-Projected Simplices in High Dimensions
    • Donoho, D.L., Tanner, J.: Neighborliness of Randomly-Projected Simplices in High Dimensions. Proc. of the Nat. Academy of Sciences 102(27), 9452-9457 (2005)
    • (2005) Proc. of the Nat. Academy of Sciences , vol.102 , Issue.27 , pp. 9452-9457
    • Donoho, D.L.1    Tanner, J.2
  • 13
    • 20744451888 scopus 로고    scopus 로고
    • Geometric representation of high dimension low sample size data
    • Hall, P., Marron, J., Neeman, A.: Geometric representation of high dimension low sample size data. J. of the Royal Statistical Society B 67(3), 427-444 (2005)
    • (2005) J. of the Royal Statistical Society B , vol.67 , Issue.3 , pp. 427-444
    • Hall, P.1    Marron, J.2    Neeman, A.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.