Automatic Annotation of Spatial Expression Patterns via Sparse Bayesian Factor Models
Figure 7
SMLR analysis on the estimated sBFA factors on data set , for two randomly selected annotation terms.
The top row shows the SMLR mixing weights on the factors, for a regularization parameter ; the x-axis represents the FA factors: the first
factors for a grid size of
×
, the next
factors for a grid size of
×
and the last
factors for a grid size of
×
. The bottom row contains histograms with the number of factors selected as relevant over
LOO-CV trials, with a cut-off value at
. Each feature appears once in the graph. The more mass concentrated at the two ends, the more consistent the classifier is in identifying relevant factors.