VC-PCR: A Prediction Method based on Supervised Variable Selection and Clustering
This addresses a specific issue in statistical modeling for researchers and practitioners dealing with correlated data, but it is incremental as it builds on existing methods for variable clustering.
The paper tackles the problem of decreased prediction accuracy in sparse linear models when predictor variables have cluster structure, proposing VC-PCR which achieves better prediction, variable selection, and clustering performance compared to competitors.
Sparse linear prediction methods suffer from decreased prediction accuracy when the predictor variables have cluster structure (e.g. there are highly correlated groups of variables). To improve prediction accuracy, various methods have been proposed to identify variable clusters from the data and integrate cluster information into a sparse modeling process. But none of these methods achieve satisfactory performance for prediction, variable selection and variable clustering simultaneously. This paper presents Variable Cluster Principal Component Regression (VC-PCR), a prediction method that supervises variable selection and variable clustering in order to solve this problem. Experiments with real and simulated data demonstrate that, compared to competitor methods, VC-PCR achieves better prediction, variable selection and clustering performance when cluster structure is present.