ndNMF | R Documentation |
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.
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.
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.
ndNMF( dat, trainlabel, r = 2, lambada = 0.1, maxIter = 1000, tol = 1e-07, log = TRUE, plotit = FALSE, verbose = FALSE, ... )
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 |
Zhilong Jia and Xiang Zhang
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
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.