Advances in Machine Learning I

Volume 262 of the series Studies in Computational Intelligence pp 227-246

An Analysis of Relevance Vector Machine Regression

  • Matti SaarelaAffiliated withDepartment of Software Systems, Tampere University of Technology
  • , Tapio ElomaaAffiliated withDepartment of Software Systems, Tampere University of Technology
  • , Keijo RuohonenAffiliated withDepartment of Mathematics, Tampere University of Technology

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The relevance vector machine (RVM) is a Bayesian framework for learning sparse regression models and classifiers. Despite of its popularity and practical success, no thorough analysis of its functionality exists. In this paper we consider the RVM in the case of regression models and present two kinds of analysis results: we derive a full characterization of the behavior of the RVM analytically when the columns of the regression matrix are orthogonal and give some results concerning scale and rotation invariance of the RVM. We also consider the practical implications of our results and present a scenario in which our results can be used to detect potential weakness in the RVM framework.


Relevance vector machine regression sparse Bayesian learning