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A popular approach to semi-supervised learning proceeds by endowing the input data with a graph structure in order to extract geometric information and incorporate it into a Bayesian framework.
Central limit theorem for additive functionals of reversible Markov processes and applications to simple exclusions
C. Kipnis and S. R. S. Varadhan · 1986
Earlier work this paper cites.
Design and analysis of computer experiments
J. Sacks, W. J. Welch, T. J. Mitchell, and H. P. Wynn · 1989
Earlier work this paper cites.
Annealing Markov chain Monte Carlo with applications to ancestral inference
C. J. Geyer and E. A. Thompson · 1995
Earlier work this paper cites.
Nonlinear dimensionality reduction by locally linear embedding
S. T. Roweis and L. K. Saul · 2000
Earlier work this paper cites.
A global geometric framework for nonlinear dimensionality reduction
J. B. Tenenbaum, V. De Silva, and J. C. Langford · 2000
Earlier work this paper cites.
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