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ICML
2007
IEEE

The rendezvous algorithm: multiclass semi-supervised learning with Markov random walks

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The rendezvous algorithm: multiclass semi-supervised learning with Markov random walks
We consider the problem of multiclass classification where both labeled and unlabeled data points are given. We introduce and demonstrate a new approach for estimating a distribution over the missing labels where data points are viewed as nodes of a graph, and pairwise similarities are used to derive a transition probability matrix P for a Markov random walk between them. The algorithm associates each point with a particle which moves between points according to P. Labeled points are set to be absorbing states of the Markov random walk, and the probability of each particle to be absorbed by the different labeled points, as the number of steps increases, is then used to derive a distribution over the associated missing label. A computationally efficient algorithm to implement this is derived and demonstrated on both real and artificial data sets, including a numerical comparison with other methods.
Arik Azran
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2007
Where ICML
Authors Arik Azran
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