On the Combination of Information-Theoretic Kernels with Generative Embeddings

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.