Model Selection with the Loss Rank Principle

Details Model Selection with the Loss Rank Principle Model Selection with the Loss Rank Principle

2010-03-02

arxiv.org/abs/1003.0516v1

Computational Statistics and Data Analysis, 54 (2010) pages 1288-1306

Computer Science

Learning

31 LaTeX pages, 1 figure

Scientific paper

A key issue in statistics and machine learning is to automatically select the "right" model complexity, e.g., the number of neighbors to be averaged over in k nearest neighbor (kNN) regression or the polynomial degree in regression with polynomials. We suggest a novel principle - the Loss Rank Principle (LoRP) - for model selection in regression and classification. It is based on the loss rank, which counts how many other (fictitious) data would be fitted better. LoRP selects the model that has minimal loss rank. Unlike most penalized maximum likelihood variants (AIC, BIC, MDL), LoRP depends only on the regression functions and the loss function. It works without a stochastic noise model, and is directly applicable to any non-parametric regressor, like kNN.

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