Analysis of Network Lasso for Semi-Supervised Regression

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...

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Bibliographic Details
Published in:arXiv.org 2018-12
Main Authors: Jung, A, Vesselinova, N
Format: Article
Language:English
Subjects:
Clustering
Empirical analysis
Error analysis
Machine learning
Message passing
Network analysis
Regression analysis
Regression models
Online Access:Get full text
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Summary: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 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 sufficiently large network flows over the empirical graph. Thus, our analysis reveals a connection between network Lasso and maximum flow problems.
ISSN:2331-8422