Open Access
December 2015 Customized training with an application to mass spectrometric imaging of cancer tissue
Scott Powers, Trevor Hastie, Robert Tibshirani
Ann. Appl. Stat. 9(4): 1709-1725 (December 2015). DOI: 10.1214/15-AOAS866


We introduce a simple, interpretable strategy for making predictions on test data when the features of the test data are available at the time of model fitting. Our proposal—customized training—clusters the data to find training points close to each test point and then fits an $\ell_{1}$-regularized model (lasso) separately in each training cluster. This approach combines the local adaptivity of $k$-nearest neighbors with the interpretability of the lasso. Although we use the lasso for the model fitting, any supervised learning method can be applied to the customized training sets. We apply the method to a mass-spectrometric imaging data set from an ongoing collaboration in gastric cancer detection which demonstrates the power and interpretability of the technique. Our idea is simple but potentially useful in situations where the data have some underlying structure.


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Scott Powers. Trevor Hastie. Robert Tibshirani. "Customized training with an application to mass spectrometric imaging of cancer tissue." Ann. Appl. Stat. 9 (4) 1709 - 1725, December 2015.


Received: 1 April 2015; Revised: 1 July 2015; Published: December 2015
First available in Project Euclid: 28 January 2016

zbMATH: 06560807
MathSciNet: MR3456351
Digital Object Identifier: 10.1214/15-AOAS866

Keywords: ‎classification‎ , clustering , local regression , Transductive learning

Rights: Copyright © 2015 Institute of Mathematical Statistics

Vol.9 • No. 4 • December 2015
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