The results below are discovered through our pilot algorithms. Let us know how we are doing!
-  C. M. Bishop, M. Svensen, and C. K. I. Williams. Developments of the generative topographic mapping. Neurocomputing, 21:203-224, 1998.
-  I. Borg and P Groenen. Modern Multidimensional Scaling: theory and applications. Springer-Verlag New York, 2005.
-  D. Broomhead and D. Lowe. Feed-forward neural networks and topographic mappings for exploratory data analysis. Complex Systems 2, pages 321-355, 1988.
-  C. Chatfield and A.J. Collins. Introduction to Multivariate Analysis. Chapman and Hall, 1980.
-  A. Dempster, N. Laird, and D. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society, Vol. 39:1-38, 1977.
-  Stefan Harmeling. Exploring model selection techniques for nonlinear dimensionality reduction. Technical report, Edinburgh University, Scotland, 2007.
-  V. de Silva J.B. Tenenbaum and J.C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290:2319-2323, 2000.
-  Merrill W. Liechty John C. Liechty and Peter Mller. Bayesian correlation estimation. Biometrika, 91:1-14, 2004.
-  T. Kohonen. Self-Organizing Maps . Springer Verlag, 1995.
-  Neil D. Lawrence. A scaled conjugate gradient algorithm for fast supervised learning. Journal of Machine Learning Research 6, page 1783?1816, 2005.
-  S.T. Roweis and L.K. Saul. Locally linear embedding. Science, 290:2323-2326, 2000.
-  Chong Ho Yu. Resampling methods: concepts, applications, and justification. Practical Assessment, Research and Evaluation, 8, 2003.
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