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Volumn 2, Issue , 2007, Pages 139-146

Visualizing pairwise similarity via Semidefinite Programming

Author keywords

[No Author keywords available]

Indexed keywords

EMBEDDINGS; IMAGE EMBEDDING; LOW DIMENSIONAL STRUCTURE; NONLINEAR MAP; OPTIMIZATION PROBLEMS; SAMPLE POINT; SEMI-DEFINITE PROGRAMMING; SEMIDEFINITE PROGRAMS; SIMILARITY MEASUREMENTS; VECTOR REPRESENTATIONS; VIEWING PARAMETERS;

EID: 84862283468     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper
Times cited : (10)

References (17)
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    • (1999) Optimization Methods and Software , vol.11 , pp. 613-623
    • Borchers, B.1
  • 3
    • 84880821665 scopus 로고    scopus 로고
    • Continuous nonlinear dimensionality reduction by kernel eigenmaps
    • Brand, M. (2003). Continuous nonlinear dimensionality reduction by kernel eigenmaps. Proc. of Int. Joint Conf. Artif. Intel..
    • (2003) Proc. of Int. Joint Conf. Artif. Intel.
    • Brand, M.1
  • 5
    • 84862277414 scopus 로고    scopus 로고
    • Maximally collapsing metric learning
    • Globerson, A., & Roweis, S. (2006). Maximally collapsing metric learning. NIPS, Volume 18.
    • (2006) NIPS , vol.18
    • Globerson, A.1    Roweis, S.2
  • 6
    • 33845594569 scopus 로고    scopus 로고
    • Dimensionality reduction by learning an invariant mapping
    • Hadsell, R., Chopra, S., & LeCun, Y. (2006). Dimensionality reduction by learning an invariant mapping. Proc. of CVPR.
    • (2006) Proc. of CVPR
    • Hadsell, R.1    Chopra, S.2    Lecun, Y.3
  • 9
    • 5044231640 scopus 로고    scopus 로고
    • Learning methods for generic object recognition with invariance to pose and lighting
    • LeCun, Y., Huang, F., & Bottou, L. (2004). Learning methods for generic object recognition with invariance to pose and lighting. Proc. of CVPR.
    • (2004) Proc. of CVPR
    • Lecun, Y.1    Huang, F.2    Bottou, L.3
  • 10
    • 0034704222 scopus 로고    scopus 로고
    • Nonlinear dimensionality reduction by locally linear embedding
    • Roweis, S., & Saul, L. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290, 2323-6.
    • (2000) Science , vol.290 , pp. 2323-2326
    • Roweis, S.1    Saul, L.2
  • 13
    • 33746329499 scopus 로고    scopus 로고
    • The fastest mixing markov process on a graph and a connection to a maximum variance unfolding problem
    • Sun, J., L., S. B., Xiao, & Diaconis, P. (2006). The fastest mixing markov process on a graph and a connection to a maximum variance unfolding problem. SIAM Review, 48, 681-699.
    • (2006) SIAM Review , vol.48 , pp. 681-699
    • Sun, J.L.1    Xiao, S.B.2    Diaconis, P.3
  • 14
    • 0034704229 scopus 로고    scopus 로고
    • A global geometric framework for nonlinear dimensionality reduction
    • Tenenbaum, J., de Silva, V., & Langford., J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290, 2319-2323.
    • (2000) Science , vol.290 , pp. 2319-2323
    • Tenenbaum, J.1    De Silva, V.2    Langford, J.C.3
  • 15
    • 33749550361 scopus 로고    scopus 로고
    • Distance metric learning for large margin nearest neighbor classification
    • Weinberger, K., Blitzer, J., & Saul, L. (2006). Distance metric learning for large margin nearest neighbor classification. NIPS, Volume 18.
    • (2006) NIPS , vol.18
    • Weinberger, K.1    Blitzer, J.2    Saul, L.3
  • 16
    • 83855163194 scopus 로고    scopus 로고
    • Nonlinear dimensionality reduction by semidefinite programming and kernel matrix factorization
    • Weinberger, K. Q., Packer, B. D., & Saul, L. K. (2005). Nonlinear dimensionality reduction by semidefinite programming and kernel matrix factorization. Proc. of AIS-TATS.
    • (2005) Proc. of AIS-TATS
    • Weinberger, K.Q.1    Packer, B.D.2    Saul, L.K.3
  • 17
    • 5044226695 scopus 로고    scopus 로고
    • Unsupervised learning of image manifolds by semidefinite programming
    • Weinberger, K. Q., & Saul, L. K. (2004). Unsupervised learning of image manifolds by semidefinite programming. Proc. of CVPR.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.