(Enter summary)
Abstract: In this paper, we study a family of semisupervised
learning algorithms for "aligning"
di#erent data sets that are characterized by
the same underlying manifold. The optimizations
of these algorithms are based on graphs
that provide a discretized approximation to
the manifold. Partial alignments of the data
sets---obtained from prior knowledge of their
manifold structure or from pairwise correspondences
of subsets of labeled examples---
are completed by integrating supervised signals... (Update)
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BibTeX entry: (Update)
J. Ham, D. Lee, and L. Saul. Semisupervised alignment of manifolds. In AISTATS, 2004. http://citeseer.ist.psu.edu/ham04semisupervised.html More
@misc{ ham04semisupervised,
author = "J. Ham and D. Lee and L. Saul",
title = "Semisupervised alignment of manifolds",
text = "J. Ham, D. Lee, and L. Saul. Semisupervised alignment of manifolds. In
AISTATS, 2004.",
year = "2004",
url = "citeseer.ist.psu.edu/ham04semisupervised.html" }
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