naive_nmf: A naive implementation of non-negative matrix factorization

In ravetools: Signal and Image Processing Toolbox for Analyzing Intracranial Electroencephalography Data

A naive implementation of non-negative matrix factorization

Description

A pure-R vanilla implementation assuming inputs are non-negative matrices without NA.

Usage

naive_nmf(x, k, tol = c(1e-04, 1e-08), max_iters = 10000, verbose = TRUE)

Arguments

x	a matrix, or can be converted into a matrix; all negative or missing values will be treated as zero
k	decomposition rank
tol	stop criteria, a numeric of two; the first number is the tolerance for root-mean-squared residuals, relative to the largest number in x; the second number is the tolerance for weight differences; any stopping criteria met will result in the stop of iteration
max_iters	maximum iterations
verbose	whether to report the progress; logical or a positive integer (of step intervals)

Value

A list of weights (non-negative template matrix W and non-negative H) and errors (root mean squared error of fitted, matrix W, and W versus their previous iteration, respectively).

Examples

x <- stats::toeplitz(.9 ^ (0:31))

nmf <- naive_nmf(x, k = 7, verbose = FALSE)

fitted <- nmf$W %*% nmf$H

oldpar <- par(mfrow = c(1, 2))
on.exit({ par(oldpar )})

image(x, zlim = c(0, 1), main = "Input")
image(fitted, zlim = c(0, 1),
      main = sprintf("Fitted with rank=%d", nmf$k))

ravetools documentation built on May 23, 2026, 9:10 a.m.