Customized training with an application to mass spectrometric imaging of cancer tissue

TL;DR

The paper proposes a customized training approach that clusters data and applies local lasso models, enhancing interpretability and adaptivity, demonstrated on mass spectrometric imaging data for gastric cancer detection.

Contribution

It introduces a simple, interpretable customized training method combining clustering with local model fitting, applicable to various supervised learning tasks.

Findings

Effective in mass spectrometric imaging data for cancer detection

Combines local adaptivity with interpretability of lasso

Potentially useful for structured data analysis

Abstract

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.