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Table 1 Comparison of NMF methods

From: A flexible R package for nonnegative matrix factorization

method

seed

metric

rank

evar

sparseness W/H

purity

entropy

niter

CPU time (seconds)

lee

nndsvd

euclidean

3

0.75

0.65/0.75

0.89

0.25

690

11.24

snmf/r

nndsvd

euclidean

3

0.75

0.65/0.75

0.97

0.10

130

4.31

brunet

nndsvd

KL

3

0.73

0.64/0.80

0.95

0.16

1110

23.60

nsNMF

nndsvd

KL

3

0.70

0.73/0.74

0.87

0.29

450

10.37

  1. Comparison of different NMF algorithms applied to the Golub dataset, using the non-negative double SVD seeding method (NNDSVD). The metric column provides the metric associated with each method: "euclidean" stands for Frobenius norm, "KL" for Kullback-Leibler divergence.
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BMC Bioinformatics

ISSN: 1471-2105

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