gnmf: Generalized non-negative matrix factorization based on Renyi...

In gnmf: Generalized Non-negative Matrix Factorization Based on Renyi Divergence

Description

Performs generalized non-negative matrix factorization based on Renyi Divergence

Usage

gnmf(V, scheme, nsteps = 2000, repeats = 20, ranks = 2, cltarget = "PATTERN",
clscheme = "Binary", reffile = "", scaling = "F", normalizing = "F", alphas = 1,
runtype = "simulation", cstepsize = 20, idealization = 1)

Arguments

V	Input data matrix
scheme	KL, Renyi, or ED
nsteps	Update steps, default 2000
repeats	Repeats, default 20
ranks	The number of components into which matrix V is to be factored, default 2 (a scalar)
cltarget	Clustering target, default 'PATTERN' (H matrix) either PATTERN or ALTERNATE
clscheme	Clustering scheme, default 'Binary', could be 'PearsonHC'
reffile	Default none
scaling	Boolean, default F
normalizing	Boolean, H matrix normalization, default 'F'
alphas	Renyi parameter, default 1.0 (a scalar), ignored if scheme is not Renyi
runtype	simulation (default) or evaluation or whole
cstepsize	Convergence test step size, default 20
idealization	Default 1

Value

H	List of pattern matrices, one for each repetition
W	List of amplitude matrices, one for each repetition

Note

Further notes...

Author(s)

Jose M. Maisog, Guoli Wang, Karthik Devarajan

References

Devarajan K. Nonnegative matrix factorization: an analytical and interpretive tool in computational biology. PLoS Comput Biol. 2008 Jul 25;4(7):e1000029.

Devarajan, K., Wang, G., Ebrahimi, N. (2011). A unified approach to nonnegative matrix factorization and probabilistic latent semantic indexing, (July 2011). Cobra Preprint Series. Working Paper 80. http://biostats.bepress.com/COBRA/Art80.

http://devarajan.fccc.edu

Examples

# Load sample data.
data(V)

# Compute NMF with 20 repeats.
result <- gnmf(V,scheme="KL")

# Extract H and W from the result.
# H and W are lists, each containing the result of 20 repeats.
H <- result$H
W <- result$W

# Get the H and W matrices of the first repeat.
H1 <- H[[1]]
W1 <- W[[1]]

Example output

gnmf documentation built on May 2, 2019, 4:18 a.m.