Subutai Ahmad, Volker Tresp
In visual processing the ability to deal with missing and noisy informa(cid:173) tion is crucial. Occlusions and unreliable feature detectors often lead to situations where little or no direct information about features is availa(cid:173) ble. However the available information is usually sufficient to highly constrain the outputs. We discuss Bayesian techniques for extracting class probabilities given partial data. The optimal solution involves inte(cid:173) grating over the missing dimensions weighted by the local probability densities. We show how to obtain closed-form approximations to the Bayesian solution using Gaussian basis function networks. The frame(cid:173) work extends naturally to the case of noisy features. Simulations on a complex task (3D hand gesture recognition) validate the theory. When both integration and weighting by input densities are used, performance decreases gracefully with the number of missing or noisy features. Per(cid:173) formance is substantially degraded if either step is omitted.