| MldaInvE {HiDimDA} | R Documentation |
Maximum uncertainty Linear Discriminant Analysis inverse matrix estimator.
Description
Builds a well-conditioned estimator for the inverse of a symmetric positive definite matrix, with a bad-conditioned or singular estimate, based on the “Maximum Uncertainty Linear Discriminant Analysis” approach of Thomaz, Kitani and Gillies (2006).
Usage
MldaInvE(M, check=TRUE, onlyMinv=TRUE,
numtol=sqrt(.Machine$double.eps))
Arguments
M |
Singular or bad-conditioned estimate of the matrix for which a well-conditioned inverse estimate is sought. |
check |
Boolean flag indicating if the symmetry of M and the sign of its eigenvalues should be check upfront. |
onlyMinv |
Boolean flag indicating if only an estimate of the matrix inverse is sought, or if a well-conditioned approximation to the matrix that M estimates should be returned as well. |
numtol |
Numerical tolerance. |
Value
If onlyMinv is set to true a matrix with the inverse estimate sought. Otherwise a list with components ME and MInvE, with a well-conditioned approximation to the matrix that M estimates and its inverse.
Author(s)
A. Pedro Duarte Silva
References
Thomaz, Kitani and Gillies (2006) “A maximum uncertainty LDA-based approach for limited sample size problems - with application to face recognition”, Journal of the Brazilian Computer Society, 12 (2), 7-18