Non-Gaussian component analysis: A semi-parametric framework for linear dimension reduction

Advances in Neural Information Processing Systems

Volumn , Issue , 2005, Pages 131-138

  Blanchard, G ^a   Sugiyama, M ^a,b   Kawanabe, M ^a   Spokoiny, V ^c   Muller K R ^a,d  

^a FRAUNHOFER FIRST   (Germany)

^b TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

^c HUMBOLDT UNIVERSITY   (Germany)

^d UNIVERSITY OF POTSDAM   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYSIS PROCESS; DE-NOISING; DIMENSION REDUCTION; ESTIMATION ERRORS; GAUSSIAN COMPONENTS; GAUSSIANS; HIGH DIMENSIONAL DATA; LINEAR DIMENSION REDUCTION; LINEAR METHODS; NON-GAUSSIAN; NON-GAUSSIAN COMPONENT ANALYSIS; NUMERICAL STUDIES; PROJECTION METHOD; SEMIPARAMETRIC;

DATA VISUALIZATION; NUMERICAL METHODS;

GAUSSIAN NOISE (ELECTRONIC);

EID: 84864031139     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. M. Belkin and P. Niyogi. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15(6):1373-1396, 2003.
2. C.M. Bishop, M. Svensen and C.K.I. Wiliams. GTM: The generative topographic mapping. Neural Computation, 10(1):215-234, 1998.
3. G. Blanchard, M. Sugiyama, M. Kawanabe, V. Spokoiny, K.-R. Müller. In search of non- Gaussian components of a high-dimensional distribution. Technical report of the Weierstrass Institute for Applied Analysis and Stochastics, 2006.
4. T.F. Cox and M.A.A. Cox. Multidimensional Scaling. Chapman & Hall, London, 2001.
5. J.H. Friedman and J.W. Tukey. A projection pursuit algorithm for exploratory data analysis. IEEE Transactions on Computers, 23(9):881-890, 1975.
6. S. Harmeling, A. Ziehe, M. Kawanabe and K.-R. Müller. Kernel-based nonlinear blind source separation. Neural Computation, 15(5):1089-1124, 2003.
7. P.J. Huber. Projection pursuit. The Annals of Statistics, 13:435-475, 1985.
8. A. Hyvärinen. Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 10(3):626-634, 1999.
9. A. Hyvärinen, J. Karhunen and E. Oja. Independent component analysis. Wiley, 2001.
10. S. Roweis and L. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500):2323-2326, 2000.
11. B. Schölkopf, A.J. Smola and K.-R. Müller. Nonlinear component analysis as a kernel Eigenvalue problem. Neural Computation, 10(5):1299-1319, 1998.
12. J.B. Tenenbaum, V. de Silva and J.C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500):2319-2323, 2000.