Analysis of Network Lasso for Semi-Supervised Regression


Alexander Jung, Natalia Vesselinova ;
Proceedings of Machine Learning Research, PMLR 89:380-387, 2019.


We apply network Lasso to semi-supervised regression problems involving network-structured data. This approach lends quite naturally to highly scalable learning algorithms in the form of message passing over an empirical graph which represents the network structure of the data. By using a simple non-parametric regression model, which is motivated by a clustering hypothesis, we provide an analysis of the estimation error incurred by network Lasso. This analysis reveals conditions on the network structure and the available training data which guarantee network Lasso to be accurate. Remarkably, the accuracy of network Lasso is related to the existence of suciently large network flows over the empirical graph. Thus, our analysis reveals a connection between network Lasso and maximum network flow problems.

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