Group-Induced Vector Spaces

The strength of classifier combination lies either in a suitable averaging over multiple experts/sources or in a beneficial integration of complementary approaches. In this paper we focus on the latter and propose the use of group-induced vector spaces (GIVSs) as a way to combine unsupervised learning with classification. In such an integrated approach, the data is first modelled by a number of groups, found by a clustering procedure. Then, a proximity function is used to measure the (dis)similarity of an object to each group. A GIVS is defined by mapping an object to a vector of proximity scores, computed with respect to the given groups. In this study, we focus on a particular aspect of using GIVSs in a mode of building a trained combiner, namely the integration of generative and discriminative methods. First, in the generative step, we model the groups by simple generative models, building the GIVS space. The classification problem is then mapped in the resulting vector space, where a discriminative classifier is trained. Our experiments show that the integrated approach leads to comparable or better results than the generative methods in the original feature spaces.