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Robust singular value decomposition (C++ backend)

Source: R/pca_interface.R
svd_robust.Rd

Compute the iteratively reweighted SVD using the high-performance C++ implementation. The interface mirrors svd_robust_R() while delegating the heavy lifting to compiled code.

Usage

svd_robust(
  x,
  ncomp,
  max_iter = 25L,
  tol = sqrt(.Machine$double.eps),
  huber_k = 1.345
)

Arguments

x

Numeric matrix for which the decomposition should be computed.

ncomp

Number of leading components to retain.

max_iter

Maximum number of reweighting iterations.

tol

Convergence tolerance applied to successive changes in the row weights and singular values.

huber_k

Tuning constant controlling the aggressiveness of the Huber weight function. Larger values down-weight fewer observations.

Value

A list containing the left and right singular vectors (u and v), the singular values (d), the final row weights (weights), and the number of iterations required for convergence (iterations).