  We pursue an early stopping technique that helps Gaussian Restricted
Boltzmann Machines (GRBMs) to gain good natural image representations in terms
of overcompleteness and data fitting. GRBMs are widely considered as an
unsuitable model for natural images because they gain non-overcomplete
representations which include uniform filters that do not represent useful
image features. We have recently found that GRBMs once gain and subsequently
lose useful filters during their training, contrary to this common perspective.
We attribute this phenomenon to a tradeoff between overcompleteness of GRBM
representations and data fitting. To gain GRBM representations that are
overcomplete and fit data well, we propose a measure for GRBM representation
quality, approximated mutual information, and an early stopping technique based
on this measure. The proposed method boosts performance of classifiers trained
on GRBM representations.
