Comparing distributions and shapes using the kernel distance
Sarang C. Joshi, Raj Varma Kommaraju, Jeff M. Phillips, Suresh Venkatasubramanian · 2011
Starting with a similarity function between objects, it is possible to define a distance metric (the kernel distance) on pairs of objects, and more generally on probability distributions over them. These distance metrics have a deep basis in functional analysis and geometric measure theory, and have a rich structure that includes an isometric embedding into a Hilbert space. They have recently been applied to numerous problems in machine learning and shape analysis.