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Issue No. 07 - July (2012 vol. 34)
ISSN: 0162-8828
pp: 1249-1262
Marco Cristani , University of Verona, Verona, and Italian Institute of Technology, Genova
Nebojsa Jojic , Microsoft Research, Redmond
Alessandro Perina , Microsoft Research, Redmond
Vittorio Murino , University of Verona, Verona, and Italian Institute of Technology, Genova
Umberto Castellani , University of Verona, Verona
ABSTRACT
A score function induced by a generative model of the data can provide a feature vector of a fixed dimension for each data sample. Data samples themselves may be of differing lengths (e.g., speech segments or other sequential data), but as a score function is based on the properties of the data generation process, it produces a fixed-length vector in a highly informative space, typically referred to as “score space.” Discriminative classifiers have been shown to achieve higher performances in appropriately chosen score spaces with respect to what is achievable by either the corresponding generative likelihood-based classifiers or the discriminative classifiers using standard feature extractors. In this paper, we present a novel score space that exploits the free energy associated with a generative model. The resulting free energy score space (FESS) takes into account the latent structure of the data at various levels and can be shown to lead to classification performance that at least matches the performance of the free energy classifier based on the same generative model and the same factorization of the posterior. We also show that in several typical computer vision and computational biology applications the classifiers optimized in FESS outperform the corresponding pure generative approaches, as well as a number of previous approaches combining discriminating and generative models.
INDEX TERMS
Hybrid generative/discriminative paradigm, variational free energy, classification.
CITATION
Marco Cristani, Nebojsa Jojic, Alessandro Perina, Vittorio Murino, Umberto Castellani, "Free Energy Score Spaces: Using Generative Information in Discriminative Classifiers", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 34, no. , pp. 1249-1262, July 2012, doi:10.1109/TPAMI.2011.241
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