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Abstract:
To classify a large number of unlabeled examples we combine a
limited number of labeled examples with a Markov random walk
representation over the unlabeled examples. The random walk
representation exploits any low dimensional structure in the data
in a robust, probabilistic manner. We develop and compare several
estimation criteria/algorithms suited to this representation.
This includes in particular multi-way classification with an
average margin criterion which permits a closed form solution.
The time scale of the random walk regularizes the representation
and can be set through a margin-based criterion favoring
unambiguous classification. We also extend this basic
regularization by adapting time scales for individual examples.
We demonstrate the approach on synthetic examples and on text
classification problems.
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