A spatial Bayesian latent factor model for image-on-image regression.

ABSTRACT: Image-on-image regression analysis, using images to predict images, is a challenging task, due to (1) the high dimensionality and (2) the complex spatial dependence structures in image predictors and image outcomes. In this work, we propose a novel image-on-image regression model, by extending a spatial Bayesian latent factor model to image data, where low-dimensional latent factors are adopted to make connections between high-dimensional image outcomes and image predictors. We assign Gaussian process priors to the spatially varying regression coefficients in the model, which can well capture the complex spatial dependence among image outcomes as well as that among the image predictors. We perform simulation studies to evaluate the out-of-sample prediction performance of our method compare

SUBMITTER: Guo C 

PROVIDER: S-EPMC8236065 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature