Nonparametric Bayesian Discrete Latent Variable Models for ...

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3.4 Dirichlet Process Mixtures of Factor Analyzers
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Figure 3.20: Change in the number of active components over the iterations for the DPMFA
model using the peak-to-peak amplitudes (top), the 12 PCA components (middle)
and the whole waveforms (bottom) with the parameter α fixed to 1.
probability of proposing new components. To check the effect of this, we also did runs
with fixed α using the PCA projections. Recall that for the run on the 12 dimensions
where α was updated, the number of active components first increased to 400, converging
to around 80 components after 2000 iterations, see Figure 3.19. Interestingly, we observe
the similar behavior even when α is fixed to 1, see Figure 3.20. For this case, the number
of components increases to 250, and converges to around 40 after 1000 iterations for the
12 dimensional representation. For the 112 dimensional representation, the number of
components initially goes up to 500, and settles around 300 after 100 iterations.
Although the number of active components is as high as 300, there are generally only
10 components with more than 5 data points. Figure 3.18 shows that the clustering
generally agrees with the results of the other experiments. We show the manual clustering
results in Figure 3.21 and the DPMFA results using the whole waveforms as inputs
in Figure 3.22 for comparison. Note that the DPMFA finds a similar clustering for most
of the data, except separating one of the big clusters into three.
Superimposed spike waveforms from each of the big clusters found by the DPMFA
model are depicted in Figure 3.23. The waveforms from the three clusters c1, c2 and c3
were assumed to belong to one big cluster in manual clustering since they have similar
amplitude characteristics. Attending to the shape of the waveforms reveals that they
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