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The Minimum Transfer Cost Principle for Model-Order Selection

  • Mario Frank
  • Morteza Haghir Chehreghani
  • Joachim M. Buhmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6911)

Abstract

The goal of model-order selection is to select a model variant that generalizes best from training data to unseen test data. In unsupervised learning without any labels, the computation of the generalization error of a solution poses a conceptual problem which we address in this paper. We formulate the principle of “minimum transfer costs” for model-order selection. This principle renders the concept of cross-validation applicable to unsupervised learning problems. As a substitute for labels, we introduce a mapping between objects of the training set to objects of the test set enabling the transfer of training solutions. Our method is explained and investigated by applying it to well-known problems such as singular-value decomposition, correlation clustering, Gaussian mixture-models, and k-means clustering. Our principle finds the optimal model complexity in controlled experiments and in real-world problems such as image denoising, role mining and detection of misconfigurations in access-control data.

Keywords

clustering generalization error transfer costs cross-validation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mario Frank
    • 1
  • Morteza Haghir Chehreghani
    • 1
  • Joachim M. Buhmann
    • 1
  1. 1.Department of Computer ScienceETH ZurichSwitzerland

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