Information Theoretical Kernels for Generative Embeddings Based on Hidden Markov Models

Many approaches to learning classifiers for structured objects (e.g., shapes) use generative models in a Bayesian framework. However, state-of-the-art classifiers for vectorial data (e.g., support vector machines) are learned discriminatively. A generative embedding is a mapping from the object space into a fixed dimensional feature space, induced by a generative model which is usually learned from data. The fixed dimensionality of these feature spaces permits the use of state of the art discriminative machines based on vectorial representations, thus bringing together the best of the discriminative and generative paradigms. Using a generative embedding involves two steps: (i) defining and learning the generative model used to build the embedding; (ii) discriminatively learning a (maybe kernel) classifier on the adopted feature space. The literature on generative embeddings is essentially focused on step (i), usually adopting some standard off-the-shelf tool (e.g., an SVM with a...

• Type: Journal
• Year: 2010
• Where: SSPR
• Authors: André F. T. Martins, Manuele Bicego, Vittorio Murino, Pedro M. Q. Aguiar, Mário A. T. Figueiredo
• Keywords: Feature Space, Generative Embeddings, Generative Model, Pattern Recognition, SSPR 2010