Samir Kanaan, Universitat Politécnica de Catalunya

Multiview data: what, why, and how

Most pattern recognition methods are designed to process data inputs from a single source. Therefore, they are not well equipped to deal with data inputs from several, possibly heterogeneous sources. However, in real life applications it is often the case that there are several sources available. In the classical data processing pipeline there are two options: to stack all the data together, with potential data coherence problems, or to discard some of the data sources. None of these options extracts the full potential of the data available. Multiview pattern recognition methods are designed to tackle with such multiple source datasets, processing each data source as a independent input while merging all the relevant information into a single result. This talk is an introduction to multiview data, possible multiview aspects and applications, and the associated multiview pattern recognition methods in the state of the art, as well as the differences between the classical, single-view data processing pipeline and a multiview data processing pipeline.

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