Truncated SVD

Truncated Singular Value Decomposition algorithm

Usage

tsvd(M, k = 1, flip_sign = c("auto", "sklearn", "none"))

Arguments

M: matrix, data to be analyzed
k: int, number of columns/features to be kept
flip_sign: character, one of the following: "auto", "sklearn", "none"

Value

matrix after dimension reduction

Details

Compute the truncated SVD for dimension reduction. Note that SVD suffers from "sign indeterminacy," which means that the signs of the output vectors could depend on the algorithm and random state. Two variants of "sign flipping methods" are implemented here, one following the sklearn implementation on Truncated SVD, another by Bro et al. (2008).

References

https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html

Bro, R., Acar, E., & Kolda, T. G. (2008). Resolving the sign ambiguity in the singular value decomposition. Journal of Chemometrics, 22(2), 135–140. https://doi.org/10.1002/cem.1122

Examples

A <- rbind(c(1,3), c(2,4))
tsvd(A)
#>          [,1]
#> [1,] 3.148096
#> [2,] 4.467164