Table 1 |
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|
Comparison of NMF methods |
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|
method |
seed |
metric |
rank |
evar |
sparseness W/H |
purity |
entropy |
niter |
CPU time (seconds) |
|
|
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|
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 |
|
|
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|
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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|
Gaujoux and Seoighe BMC Bioinformatics 2010 11:367 doi:10.1186/1471-2105-11-367 |
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