Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions

Understand

Kernel mean embeddings have recently attracted the attention of the machine learning community.

• They map measures $\mu$ from some set $M$ to functions in a reproducing kernel Hilbert space (RKHS) with kernel $k$.
• The RKHS distance of two mapped measures is a semi-metric $d_k$ over $M$.
• We study three questions.