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.
