SplitKnockoff (version 2.1)

canonicalSVD: singular value decomposition

Description

Computes a reduced SVD without sign ambiguity. Our convention is that the sign of each vector in U is chosen such that the coefficient with largest absolute value is positive.

Usage

canonicalSVD(X)

Value

S

U

V

Arguments

X

the input matrix

Examples

nu = 10
n = 350
m = 100
A_gamma <- rbind(matrix(0,n,m),-diag(m)/sqrt(nu))
svd.result = canonicalSVD(A_gamma)
S <- svd.result$S
S <- diag(S)
V <- svd.result$V