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. 2017 Aug 7;12(8):e0182130. doi: 10.1371/journal.pone.0182130

Fig 3. Classification accuracy with one-, two- and three-dimensional latent space in our GTM algorithm.

Fig 3

(A) Normalized histograms exhibit the angular distances for the one- and two-dimensional latent space under different SNRs. (B) The sizes of classes are for different latent space dimensions with varying SNRs. The label ‘GTM_D’ in (A) and (B) represents the number of dimensions. GTM_1D denotes that 500 points in one dimensional latent space were sampled in the GTM algorithm. GTM_2D denotes that 100 points in one dimension and 5 points in the other dimension, a total of 500 points, were sampled by the GTM algorithm. GTM_3D denotes that 20 points in the first dimension and 5 points in each of the other two dimensions, giving a total 500 points, were sampled in the GTM algorithm.