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egpivo
SpatPCA:Regularized Principal Component Analysis for Spatial Data
Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigen-functions by using the alternating direction method of multipliers algorithm (Wang and Huang, 2017, <DOI:10.1080/10618600.2016.1157483>). The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D.
Maintained by Wen-Ting Wang. Last updated 7 months ago.
admmcovariance-estimationeigenfunctionslassomatrix-factorizationpcarcpparmadillorcppparallelregularizationspatialspatial-data-analysissplinesopenblascppopenmp
20 stars 5.53 score 17 scripts