  Hutter (2007) recently introduced the loss rank principle (LoRP) as a
generalpurpose principle for model selection. The LoRP enjoys many attractive
properties and deserves further investigations. The LoRP has been well-studied
for regression framework in Hutter and Tran (2010). In this paper, we study the
LoRP for classification framework, and develop it further for model selection
problems in unsupervised learning where the main interest is to describe the
associations between input measurements, like cluster analysis or graphical
modelling. Theoretical properties and simulation studies are presented.
