Principal component analysis (PCA) is a widely used method for dimension reduction. In high-dimensional data, the "signal" eigenvalues corresponding to weak principal components (PCs) do not ...
Two methods are presented for efficiently computing the eigenvalues of the finite-difference Laplacian. One method embeds the region considered in a rectangle. The other method is applicable when the ...
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