rank.rSVDdpd: Rank Estimation for Robust Singular Value Decomposition
Description
rank.rSVDdpd estimates the optimal rank of a given matrix under
robust SVD using Density Power Divergence (DPD) criteria.
Usage
rank.rSVDdpd(X, alpha = 0.5, maxrank = NULL)
Value
A named integer vector of length 3, giving the estimated ranks according
to each criterion:
DIC — estimated rank from DIC.
RCC — estimated rank from RCC.
DICMR — estimated rank from DICMR (recommended).
Arguments
X
matrix, the data matrix for which robust rank estimation is required.
alpha
numeric, robustness parameter between 0 and 1 (default 0.5).
Controls the trade-off between robustness and efficiency in the DPD measure.
maxrank
integer, maximum rank to be considered. Defaults to
min(dim(X)).
Details
The function computes three penalized criteria for rank determination:
DIC — Divergence Information Criterion.
RCC — Robust Cross-Validation Criterion.
DICMR — Modified Divergence Information Criterion with Matrix Rank
penalty (recommended).
The function computes a full robust SVD (up to maxrank) using
rSVDdpd. It then evaluates the DPD divergence at different
candidate ranks and applies penalty adjustments for model complexity.
The final estimated rank minimizes the penalized criterion.