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Volumn 148, Issue , 2006, Pages 505-512

Learning low-rank kernel matrices

Author keywords

[No Author keywords available]

Indexed keywords

BREGMAN MATRIX; DIVERGENCES; FULL-RANK KERNELS; LOW RANK KERNELS;

EID: 34250754976     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1143844.1143908     Document Type: Conference Paper
Times cited : (103)

References (15)
  • 1
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    • Predictive low-rank decomposition for kernel methods
    • Bach, F., & Jordan, M. (2005). Predictive low-rank decomposition for kernel methods. Proc. ICML-2005.
    • (2005) Proc. ICML-2005
    • Bach, F.1    Jordan, M.2
  • 2
    • 33846349887 scopus 로고
    • A hierarchical O(n log n) force calculation algorithm
    • Barnes, J., & Hut, P. (1986). A hierarchical O(n log n) force calculation algorithm. Nature, 324, 446-449.
    • (1986) Nature , vol.324 , pp. 446-449
    • Barnes, J.1    Hut, P.2
  • 3
    • 49949144765 scopus 로고
    • The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming
    • Bregman, L. (1967). The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming. USSR Comp. Mathematics and Mathematical Physics, 7, 200-217.
    • (1967) USSR Comp. Mathematics and Mathematical Physics , vol.7 , pp. 200-217
    • Bregman, L.1
  • 6
    • 0041494125 scopus 로고    scopus 로고
    • Efficient SVM training using low-rank kernel representations
    • Fine, S., & Scheinberg, K. (2001). Efficient SVM training using low-rank kernel representations. Journal of Machine Learning Research, 2, 243-264.
    • (2001) Journal of Machine Learning Research , vol.2 , pp. 243-264
    • Fine, S.1    Scheinberg, K.2
  • 8
    • 0000396658 scopus 로고
    • A fast algorithm for particle simulations
    • Greengard, L., & Rokhlin, V. (1987). A fast algorithm for particle simulations. J. Comput. Phys., 73, 325-348.
    • (1987) J. Comput. Phys , vol.73 , pp. 325-348
    • Greengard, L.1    Rokhlin, V.2
  • 9
    • 0344153904 scopus 로고    scopus 로고
    • Computing the nearest correlation matrix - a problem from finance
    • Higham, N. (2002). Computing the nearest correlation matrix - a problem from finance. IMA J. Numerical Analysis, 22, 329-343.
    • (2002) IMA J. Numerical Analysis , vol.22 , pp. 329-343
    • Higham, N.1
  • 11
  • 14
    • 21844471282 scopus 로고    scopus 로고
    • Matrix exponentiated gradient updates for online learning and Bregman projection
    • Tsuda, K., Rätsch, G., & Warmuth, M. (2005). Matrix exponentiated gradient updates for online learning and Bregman projection. Journal of Machine Learning Research, 6, 995-1018.
    • (2005) Journal of Machine Learning Research , vol.6 , pp. 995-1018
    • Tsuda, K.1    Rätsch, G.2    Warmuth, M.3
  • 15
    • 14344251006 scopus 로고    scopus 로고
    • Learning a kernel matrix for nonlinear dimensionality reduction
    • Weinberger, K., Sha, F., & Saul, L. (2004). Learning a kernel matrix for nonlinear dimensionality reduction. Proc. ICML-2004.
    • (2004) Proc. ICML-2004
    • Weinberger, K.1    Sha, F.2    Saul, L.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.