The neutral point method for kernel-based combination of disjoint training data in multi-modal pattern recognition

Authors:
David Windridge;Vadim Mottl;Alexander Tatarchuk;Andrey Eliseyev
Affiliations:
Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK;Computing Center of the Russian Academy of Sciences, Moscow, Russia;Computing Center of the Russian Academy of Sciences, Moscow, Russia;Moscow Institute of Physics and Technology, Moscow Region, Russia
Venue:
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Year:
2007

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Abstract

Multiple modalities present potential difficulties for kernel-based pattern recognition in consequence of the lack of inter-modal kernel measures. This is particularly apparent when training sets for the differing modalities are disjoint. Thus, while it is always possible to consider the problem at the classifier fusion level, it is conceptually preferable to approach the matter from a kernel-based perspective. By interpreting the aggregate of disjoint training sets as an entire data set with missing inter-modality measurements to be filled in by appropriately chosen substitutes, we arrive at a novel kernel-based technique, the neutral-point method. On further theoretical analysis, it transpires that the method is, in structural terms, a kernel-based analog of the well-known sum rule combination scheme. We therefore expect the method to exhibit similar error-canceling behavior, and thus constitute a robust and conservative strategy for the treatment of kernel-based multi-modal data.