Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions

Understand

Low-dimensional embedding, manifold learning, clustering, classification, and anomaly detection are among the most important problems in machine learning.

• The existing methods usually consider the case when each instance has a fixed, finite-dimensional feature representation.
• Here we consider a different setting.
• We assume that each instance corresponds to a continuous probability distribution.