Generalising maximum mean discrepancy: kernelised functional Bregman divergences
For machine learning practitioners, this provides a new theoretical framework connecting Bregman divergences with kernel methods, potentially improving estimation and modelling tasks.
The paper generalises maximum mean discrepancy by introducing kernelised functional Bregman divergences in Hilbert spaces, enabling easy estimation via kernel mean embeddings. It demonstrates applications in clustering, universal estimation, robust estimation, and generative modelling.
Bregman divergences play a pivotal role in statistics, machine learning and computational information geometry. Particularly in the context of machine learning, they are central to clustering, exponential families, parameter estimation and optimisation, among other things. Despite this, the full toolkit of Hilbert spaces and in particular reproducing kernel Hilbert spaces have not been systematically developed and applied to functional Bregman divergences, where points are functions rather than finite-dimensional parameter vectors. While other types of functional Bregman divergences have been studied, these are typically in a Banach space rather than more directly aligned with kernel methods and Hilbert-space geometry commonly used in machine learning. We consider functional Bregman divergences on a Hilbert space, where the self-dual pairing and Riesz representer afford us particularly convenient calculus. Further specialising Bregman generators as a composition involving a kernel mean embedding makes such divergences easy to estimate. We discuss applications in clustering, universal estimation, robust estimation and generative modelling, and contrast our approach with other types of Bregman divergences.