Semi-supervised regression using Hessian energy with an application to semi-supervised dimensionality reduction

Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference

Volumn , Issue , 2009, Pages 979-987

  Kim, Kwang In ^a   Steinke, Florian ^b,c   Hein, Matthias ^a  

^a SAARLAND UNIVERSITY   (Germany)

^b SIEMENS AG   (Germany)

^c MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; REGRESSION ANALYSIS;

DIMENSIONALITY REDUCTION; ENERGY; FEATURE SPACE; GEODESIC DISTANCES; GRAPH LAPLACIAN; LOW DIMENSIONAL; POWER; SECOND ORDERS; SEMI-SUPERVISED; SUBMANIFOLDS;

LAPLACE TRANSFORMS;

EID: 84860192651     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. M. Belkin and P. Niyogi. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15(6):1373-1396, 2003.
2. M. Belkin and P. Niyogi. Semi-supervised learning on manifolds. Machine Learning, 56:209-239, 2004.
3. D. Donoho and C. Grimes. Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data. Proc. of the National Academy of Sciences, 100(10):5591-5596, 2003.
4. J. Eells and L. Lemaire. Selected topics in harmonic maps. AMS, Providence, RI, 1983.
5. M. Hein. Uniform convergence of adaptive graph-based regularization. In G. Lugosi and H. Simon, editors, Proc. of the 19th Conf. on Learning Theory (COLT), pages 50-64, Berlin, 2006. Springer.
6. R. Irony, D. Cohen-Or, and D. Lischinski. Colorization by example. In Proc. Eurographics Symposium on Rendering, pages 201-210, 2005.
7. J. M. Lee. Riemannian Manifolds - An introduction to curvature. Springer, New York, 1997.
8. A. Levin, D. Lischinski, and Y. Weiss. Colorization using optimization. In Proc. SIGGRAPH, pages 689-694, 2004.
9. Q. Luan, F. Wen, D. Cohen-Or, L. Liang, Y.-Q. Xu, and H.-Y. Shum. Natural image colorization. In Proc. Eurographics Symposium on Rendering, pages 309-320, 2007.
10. G. Peters. Efficient pose estimation using view-based object representations. Machine Vision and Applications, 16(1):59-63, 2004.
11. F. Steinke and M. Hein. Non-parametric regression between Riemannian manifolds. In Advances in Neural Information Processing Systems, pages 1561-1568, 2009.
12. J. J. Verbeek and N. Vlassis. Gaussian fields for semi-supervised regression and correspondence learning. Pattern Recognition, 39:1864-1875, 2006.
13. X. Yang, H. Fu, H. Zha, and J. Barlow. Semi-supervised nonlinear dimensionality reduction. In Proc. of the 23rd international conference on Machine learning, pages 1065-1072, New York, NY, USA, 2006. ACM.