MWPCR stands for Multiscale Weighted Principal Component Regression. Please refer to the paper "MWPCR: Multiscale Weighted Principal Component Regression for High-dimensional Prediction" for more details about the methods and models.
In MWPCR, we build an importance score weight matrix for the selection of individual features at each location and a spatial weight matrix for the incorporation of the spatial pattern of feature vectors. We integrate the importance score weights with the spatial weights in order to recover the low dimensional structure of high dimensional features.
Please refer to the document "MWPCR Manual_v1.2.pdf" for a detailed instruction, including examples for GUI and script-based running.
Please also see https://www.nitrc.org/projects/mwpcr