MWPCR: Multiscale Weighted Principal Component Regression for High-dimensional Prediction.

ABSTRACT: We propose a multiscale weighted principal component regression (MWPCR) framework for the use of high dimensional features with strong spatial features (e.g., smoothness and correlation) to predict an outcome variable, such as disease status. This development is motivated by identifying imaging biomarkers that could potentially aid detection, diagnosis, assessment of prognosis, prediction of response to treatment, and monitoring of disease status, among many others. The MWPCR can be regarded as a novel integration of principal components analysis (PCA), kernel methods, and regression models. In MWPCR, we introduce various weight matrices to prewhitten high dimensional feature vectors, perform matrix decomposition for both dimension reduction and feature extraction, and build a prediction m

SUBMITTER: Zhu H 

PROVIDER: S-EPMC5693262 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature