Chapter

Similarity-Based Pattern Analysis and Recognition

Part of the series Advances in Computer Vision and Pattern Recognition pp 67-83

On the Combination of Information-Theoretic Kernels with Generative Embeddings

  • Pedro M. Q. AguiarAffiliated withInstituto de Sistemas e Robótica, Instituto Superior Técnico
  • , Manuele BicegoAffiliated withDipartimento di Informatica, University of Verona
  • , Umberto CastellaniAffiliated withDipartimento di Informatica, University of Verona
  • , Mário A. T. FigueiredoAffiliated withInstituto de Telecomunicações, Instituto Superior Técnico Email author 
  • , André T. MartinsAffiliated withInstituto de Telecomunicações, Instituto Superior Técnico
  • , Vittorio MurinoAffiliated withDipartimento di Informatica, University of Verona
  • , Alessandro PerinaAffiliated withMicrosoft Research
  • , Aydın UlaşAffiliated withDipartimento di Informatica, University of Verona

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Abstract

Classical methods to obtain classifiers for structured objects (e.g., sequences, images) are based on generative models and adopt a classical generative Bayesian framework. To embrace discriminative approaches (namely, support vector machines), the objects have to be mapped/embedded onto a Hilbert space; one way that has been proposed to carry out such an embedding is via generative models (maybe learned from data). This type of hybrid discriminative/generative approach has been recently shown to outperform classifiers obtained directly from the generative model upon which the embedding is built.

Discriminative approaches based on generative embeddings involve two key components: a generative model used to define the embedding; a discriminative learning algorithms to obtain a (maybe kernel) classifier. The literature on generative embedding is essentially focused on defining the embedding, and some standard off-the-shelf kernel and learning algorithm are usually adopted. Recently, we have proposed a different approach that exploits the probabilistic nature of generative embeddings, by using information-theoretic kernels defined on probability distributions. In this chapter, we review this approach and its building blocks. We illustrate the performance of this approach on two medical applications.