opt.rank: Finds the optimal rank for non-negative matrix factorization...

In fabisearch: Change Point Detection in High-Dimensional Time Series Networks

Finds the optimal rank for non-negative matrix factorization (NMF)

Description

This function finds the optimal rank for non-negative matrix factorization (NMF).

Usage

opt.rank(Y, nruns = 50, algtype = "brunet")

Arguments

Y	An input multivariate time series in matrix format, with variables organized in columns and time points in rows. All entries in Y must be positive.
nruns	A positive integer with default value equal to 50. It is used to define the number of runs in the NMF function.
algtype	A character string, which defines the algorithm to be used in the NMF function. By default it is set to "brunet". See the "Algorithms" section of nmf for more information on the available algorithms.

Value

A positive integer representing the optimal rank.

Author(s)

Martin Ondrus, [e-mail], Ivor Cribben, [e-mail]

References

"Factorized Binary Search: a novel technique for change point detection in multivariate high-dimensional time series networks", Ondrus et al. (2021), <arXiv:2103.06347>.

Examples

## Finding the optimal rank for an input data set "sim2" with nruns = 4
set.seed(123)
opt.rank(sim2, nruns = 4)
# [1] "Finding optimal rank"
# [1] "Optimal rank: 2"
# [1] 2

fabisearch documentation built on Jan. 12, 2023, 5:08 p.m.