Large Data and Zero Noise Limits of Graph-Based Semi-Supervised Learning Algorithms

23 May 2018Matthew M. DunlopDejan SlepčevAndrew M. StuartMatthew Thorpe

Scalings in which the graph Laplacian approaches a differential operator in the large graph limit are used to develop understanding of a number of algorithms for semi-supervised learning; in particular the extension, to this graph setting, of the probit algorithm, level set and kriging methods, are studied. Both optimization and Bayesian approaches are considered, based around a regularizing quadratic form found from an affine transformation of the Laplacian, raised to a, possibly fractional, exponent... (read more)

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