Adaptive tree kernel by multinomial generative topographic mapping

Learning the kernel function from data is a challenging open issue in structured data processing. In the paper, we propose a novel adaptive kernel, defined over a generative learning model, that exploits a novel multinomial extension of the Generative Topographic Mapping for Structured Data (GTM-SD). We show how the proposed kernel effectively exploits the GTM-SD continuity and smoothness properties to provide dense kernels characterized by an high discriminative power even with small topographic maps. Experimental evaluations on challenging structured XML document repositories show the effectiveness of the proposed approach against state-of-the-art syntactic and adaptive convolutional kernels.