Combining information theoretic kernels with generative embeddings for classification

  • Authors:

  • Manuele Bicego;AydıN Ulaş;Umberto Castellani;Alessandro Perina;Vittorio Murino;André F. T. Martins;Pedro M. Q. Aguiar;MáRio A. T. Figueiredo

  • Affiliations:

  • Dipartimento di Informatica, University of Verona, Verona, Italy;Dipartimento di Informatica, University of Verona, Verona, Italy;Dipartimento di Informatica, University of Verona, Verona, Italy;Microsoft Research, Redmond, WA, USA;Dipartimento di Informatica, University of Verona, Verona, Italy and Istituto Italiano di Tecnologia - IIT, Genova, Italy;Instituto de Telecomunicaçíes, Instituto Superior Técnico, Lisboa, Portugal;Instituto de Sistemas e Robótica, Instituto Superior Técnico, Lisboa, Portugal;Instituto de Telecomunicaçíes, Instituto Superior Técnico, Lisboa, Portugal

  • Venue:
  • Neurocomputing
  • Year:
  • 2013

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Abstract

Classical approaches to learn classifiers for structured objects (e.g., images, sequences) use generative models in a standard Bayesian framework. To exploit the state-of-the-art performance of discriminative learning, while also taking advantage of generative models of the data, generative embeddings have been recently proposed as a way of building hybrid discriminative/generative approaches. A generative embedding is a mapping, induced by a generative model (usually learned from data), from the object space into a fixed dimensional space, adequate for discriminative classifier learning. Generative embeddings have been shown to often outperform the classifiers obtained directly from the generative models upon which they are built. Using a generative embedding for classification involves two main steps: (i) defining and learning a generative model and using it to build the embedding; (ii) discriminatively learning a (maybe kernel) classifier with the embedded data. The literature on generative embeddings is essentially focused on step (i), usually taking some standard off-the-shelf tool for step (ii). Here, we adopt a different approach, by focusing also on the discriminative learning step. In particular, we exploit the probabilistic nature of generative embeddings, by using kernels defined on probability measures; in particular we investigate the use of a recent family of non-extensive information theoretic kernels on the top of different generative embeddings. We show, in different medical applications that the approach yields state-of-the-art performance.