SCOPUS 정보 검색 플랫폼

Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference

Volumn , Issue , 2008, Pages

Colored maximum variance unfolding

(4) Song, Le a Smola, Alex a Borgwardt, Karsten b Gretton, Arthur c

a CSIRO (Australia)

b UNIVERSITY OF CAMBRIDGE (United Kingdom)

c MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CLASS LABELS; DEPENDENCE MEASURES; DIMENSIONALITY REDUCTION; EMBEDDINGS; LOW-DIMENSIONAL REPRESENTATION; MAXIMUM VARIANCE; SIDE INFORMATION; STATISTICAL DEPENDENCE; UNFOLDINGS;

LEARNING SYSTEMS;

EID: 85162021460 PISSN: None EISSN: None Source Type: Conference Proceeding
DOI: None Document Type: Conference Paper

Times cited : (78)

References (8)

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- Learning a kernel matrix for nonlinear dimensionality reduction
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- (2004) Proceedings of the 21st International Conference on Machine Learning
- Weinberger, K.Q.¹ Sha, F.² Saul, L.K.³

2
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- The fastest mixing Markov process on a graph and a connection to a maximum variance unfolding problem
- DOI 10.1137/S0036144504443821
- J. Sun, S. Boyd, L. Xiao, and P. Diaconis. The fastest mixing markove process on a graph and a connection to a maximum variance unfolding problem. SIAM Review, 48(4):681-699, 2006. (Pubitemid 44817174)
- (2006) SIAM Review , vol.48 , Issue.4 , pp. 681-699
- Sun, J.¹ Boyd, S.² Xiao, L.³ Diaconis, P.⁴

3
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- Measuring statistical dependence with Hilbert-Schmidt norms
- S. Jain, H. U. Simon, and E. Tomita, editors, Berlin, Germany. Springer-Verlag
- A. Gretton, O. Bousquet, A.J. Smola, and B. Schölkopf. Measuring statistical dependence with Hilbert-Schmidt norms. In S. Jain, H. U. Simon, and E. Tomita, editors, Proceedings Algorithmic Learning Theory, pages 63-77, Berlin, Germany, 2005. Springer-Verlag.
- (2005) Proceedings Algorithmic Learning Theory , pp. 63-77
- Gretton, A.¹ Bousquet, O.² Smola, A.J.³ Schölkopf, B.⁴

4
- 85158052392
- Graph laplacian regularization for large-scale semidefinte programming
- K. Weinberger, F. Sha, Q. Zhu, and L. Saul. Graph laplacian regularization for large-scale semidefinte programming. In Neural Information Processing Systems, 2006.
- (2006) Neural Information Processing Systems
- Weinberger, K.¹ Sha, F.² Zhu, Q.³ Saul, L.⁴

5
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- Dimensionality reduction for supervised learning with reproducing kernel hilbert spaces
- K. Fukumizu, F. R. Bach, and M. I. Jordan. Dimensionality reduction for supervised learning with reproducing kernel hilbert spaces. J. Mach. Learn. Res., 5:73-99, 2004.
- (2004) J. Mach. Learn. Res. , vol.5 , pp. 73-99
- Fukumizu, K.¹ Bach, F.R.² Jordan, M.I.³

6
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- Neighbourhood component analysis
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- Goldberger, J.¹ Roweis, S.² Hinton, G.³ Salakhutdinov, R.⁴

7
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- Supervised feature selection via dependence estimation
- L. Song, A. Smola, A. Gretton, K. Borgwardt, and J. Bedo. Supervised feature selection via dependence estimation. In Proc. Intl. Conf. Machine Learning, 2007.
- (2007) Proc. Intl. Conf. Machine Learning
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8
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- A dependence maximization view of clustering
- L. Song, A. Smola, A. Gretton, and K. Borgwardt. A dependence maximization view of clustering. In Proc. Intl. Conf. Machine Learning, 2007.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.