Local dimensionality reduction

Advances in Neural Information Processing Systems

Volumn , Issue , 1998, Pages 633-639

  Schaal, Stefan ^a,b,d   Vijayakumar, Scthu ^a,c   Atkeson, Christopher G ^d  

^a ERATO Kawato Dynamic Brain Project (JST)   (Japan)

^b UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

^c TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

^d Georgia Institute of Technology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA HANDLING; FACTOR ANALYSIS; LEAST SQUARES APPROXIMATIONS; MONTE CARLO METHODS; MULTIVARIANT ANALYSIS; REGRESSION ANALYSIS;

DIMENSIONALITY REDUCTION; HIGH DIMENSIONAL DATA; JOINT DISTRIBUTIONS; LOCAL VERSIONS; LOW DIMENSIONAL; PARTIAL LEAST SQUARES REGRESSION; PRINCIPLE COMPONENT REGRESSION;

PRINCIPAL COMPONENT ANALYSIS;

EID: 0000940247     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Atkeson, C. G., Moore, A. W., & Schaal, S, (1997a). "Locally weighted learning." Artificial Intelligence Review, 11, 1-5, pp. 11-73.
2. Atkeson, C. G., Moore, A. W., & Schaal, S, (1997c). "Locally weighted learning for control." Artificial Intelligence Review, 11, 1-5, pp.75-113.
3. Belsley, D. A, Kuh, E., & Welsch, R. E, (1980). Regression diagnostics: Identifying influential data and sources of collinearity. New York:.
4. Wiley. Everitt, B. S, (1984). An introduction to latent variable models. London: Chapman and Hall.
5. Fahlman, S. E., Lebiere, C, (1990). "The cascadeorrelation learning architecture." In: Touretzky, D. S. [Ed.), Advances in Neural Information Processing Systems//, pp.524-532. Morgan Kaufmann.
6. Frank, I. E., & Friedman, J. H, (1993). "A statistical view of some chemometric regression tools." Tech- nometrics, 35, 2, pp.109-135.
7. Geman, S., Bienenstock, E., & Doursat, R, (1992). 'Neural networks and the bias/variance dilemma." Neural Computation, 4, pp. 1 -58.
8. Horn, R. A., & Johnson, C. R, (1994). Matrix analysis. Press Syndicate of the University of Cambridge.
9. Jordan, M. I., & Jacobs, R, (1994). "Hierarchical mix-tures of experts and the EM algorithm." Neural Computationi, 6, 2, pp. 181-214.
10. Massy, W. F, (1965). "Principle component regression in exploratory statistical research." Journal of the American Statistical Association, 60, pp.234-246.
11. Rubin, D. B., & Thayer, D. T, (1982). "EM algorithms for ML factor analysis." Psychometrika, 47, 1, 69-76.
12. Schaal, S., & Atkeson, C. G, (in press). "Constructive incremental learning from only local information." Neural Computation.
13. Scott, D. W, (1992). Multivariate Density Estimation. New York: Wiley.
14. Vijayakumar, S., & Schaal, S, (1997). "Local dimensionality reduction for locally weighted learning." In: International Conference on Computational Intelligence in Robotics and Automation, pp.220-225, Monteray, CA, July 10-11, 1997.
15. Wold, H. (1975). "Soft modeling by latent variables: The nonlinear iterative partial least squares approach." In: Gani, J. (Ed.), Perspectives in Probability and Statistics, Papers in Honour of M. S. Bartlett. Aca. Press.
16. Xu, L., Jordan, M. I., & Hinton, G. E, (1995). "An alternative model for mixture of experts." In: Tesauro, G., Touretzky, D. S., & Leen, T. K. (Eds.), Advances in Neural Information Processing Systems 7, pp.633-640. Cambridge, MA: MIT Press.