DNMF (version 1.4.2)

ndNMF: a new discriminant Non-Negative Matrix Factorization (dNMF)

Description

The ndNMF algorithm with the additional Fisher criterion on the cost function of conventional NMF was designed to increase class-related discriminating power.

This algorithm is based on articles.

1. Kim, Bo-Kyeong, and Soo-Young Lee. "Spectral Feature Extraction Using dNMF for Emotion Recognition in Vowel Sounds." Neural Information Processing. Springer Berlin Heidelberg, 2013.

2. Lee, Soo-Young, Hyun-Ah Song, and Shun-ichi Amari. "A new discriminant NMF algorithm and its application to the extraction of subtle emotional differences in speech." Cognitive neurodynamics 6.6 (2012): 525-535.

Usage

ndNMF(
  dat,
  trainlabel,
  r = 2,
  lambada = 0.1,
  maxIter = 1000,
  tol = 1e-07,
  log = TRUE,
  plotit = FALSE,
  verbose = FALSE,
  ...
)

Arguments

dat

a matrix with gene in row and sample in column

trainlabel

the label of sample, like c(1,1,2,2,2)

r

the dimension of expected reduction dimension, with the default value 2

lambada

a relative weighting factor for the discriminant. Default 0.1

maxIter

the maximum iteration of update rules, with the default value 1000

tol

the toleration of coverange, with the default value 1e-7

log

log2 data. Default is TRUE.

plotit

whether plot H (V=WH). Default: FALSE.

verbose

TRUE

...

to gplots::heatmap.2

Examples

# NOT RUN {
dat <- rbind(matrix(c(rep(3, 16), rep(8, 24)), ncol=5), 
matrix(c(rep(5, 16), rep(5, 24)), ncol=5), 
matrix(c(rep(18, 16), rep(7, 24)), ncol=5)) + 
matrix(runif(120,-1,1), ncol=5)
trainlabel <- c(1,1,2,2,2)

res <- ndNMF(dat, trainlabel, r=2, lambada = 0.1)
res$H
res$rnk

# }