Regularized Principal Component Analysis for Spatial Data
A new regularization approach to estimate the leading spatial patterns via smoothness and sparseness penalties, and spatial predctions for spatial data that may be irregularly located in space, and obtain the spatial prediction at the designated locations.
|License:||GPL version 2 or newer|
Wen-Ting Wang <email@example.com> and Hsin-Cheng Huang <firstname.lastname@example.org>
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