Skip to main content

Advertisement

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.