  In this paper we examine data fusion methods for multi-view data
classification. We present a decision concept which explicitly takes into
account the input multi-view structure, where for each case there is a
different subset of relevant views. This data fusion concept, which we dub
Mixture of Views, is implemented by a special purpose neural network
architecture. It is demonstrated on the task of classifying breast
microcalcifications as benign or malignant based on CC and MLO mammography
views. The single view decisions are combined by a data-driven decision,
according to the relevance of each view in a given case, into a global
decision. The method is evaluated on a large multi-view dataset extracted from
the standardized digital database for screening mammography (DDSM). The
experimental results show that our method outperforms previously suggested
fusion methods.
