A naive implementation of non-negative matrix factorization

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))