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Creators/Authors contains: "Hoffmann, Franca"

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  1. ; ; ; (, Applied and Computational Harmonic Analysis)
    Full Text Available 
  2. ; ; (, Journal of machine learning research)
    Accepted Manuscript Full Text Available
  3. ; ; ; (, Journal of machine learning research)
    null (Ed.)
    Graph-based semi-supervised learning is the problem of propagating labels from a small number of labelled data points to a larger set of unlabelled data. This paper is concerned with the consistency of optimization-based techniques for such problems, in the limit where the labels have small noise and the underlying unlabelled data is well clustered. We study graph-based probit for binary classification, and a natural generalization of this method to multi-class classification using one-hot encoding. The resulting objective function to be optimized comprises the sum of a quadratic form defined through a rational function of the graph Laplacian, involving only the unlabelled data, and a fidelity term involving only the labelled data. The consistency analysis sheds light on the choice of the rational function defining the optimization. 
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    Accepted Manuscript Full Text Available
  4. ; ; ; (, SIAM Journal on Applied Dynamical Systems)
    null (Ed.)
    Full Text Available