Handling Multimodal Information Fusion with Missing Observations Using the Neutral Point Substitution Method
We have previously introduced, in purely theoretical terms, the notion of neutral point substitution for missing kernel data in multimodal problems. In particular, it was demonstrated that when modalities are maximally disjoint, the method is precisely equivalent to the Sum rule decision scheme. As well as forging an intriguing analogy between multikernel and decision-combination methods, this finding means that the neutral-point method should exhibit a degree of resilience to class misattribution within the individual classifiers through the relative cancelling of combined estimation errors (if sufficiently decorrelated). However, the case of completely disjoint modalities is unrepresentative of the general missing data problem. We here set out to experimentally test the notion of neutral point substitution in a realistic experimental scenario with partially-disjoint data to establish the practical application of the method. The tested data consists in multimodal Biometric measurements of individuals in which the missing-modality problem is endemic. We hence test a SVM classifier under both the modal decision fusion and neutral point-substitution paradigms, and find that, while error cancellation is indeed apparent, the genuinely multimodal approach enabled by the neutral-point method is superior by a significant factor.