crossValidate: Cross-validation for NMF

In zdebruine/RcppML: Rcpp Machine Learning Library

Cross-validation for NMF

Description

Find an "optimal" rank for a Non-Negative Matrix Factorization using cross-validation. Returns a data.frame with class nmfCrossValidate. Plot results using the plot class method.

Usage

crossValidate(data, k, reps = 3, n = 0.05, verbose = FALSE, ...)

## S3 method for class 'nmfCrossValidate'
plot(x, ...)

Arguments

data	dense or sparse matrix of features in rows and samples in columns. Prefer matrix or Matrix::dgCMatrix, respectively
k	array of factorization ranks to test
reps	number of independent replicates to run
n	fraction of values to handle as missing (default is 5%, or 0.05)
verbose	should updates be displayed when each factorization is completed
...	parameters to RcppML::nmf, not including data or k
x	nmfCrossValidate object, the result of crossValidate

Details

A random speckled pattern of values is masked off during model fitting, and the mean squared error of prediction is evaluated after the model has reached the desired tolerance. The rank at which the model achieves the lowest error (best prediction accuracy) is the optimal rank.

Value

data.frame with class nmfCrossValidate with columns rep, k, and value

See Also

nmf

zdebruine/RcppML documentation built on Sept. 13, 2023, 11:44 p.m.