Linear-time Learning on Distributions with Approximate Kernel Embeddings

Understand

Many interesting machine learning problems are best posed by considering instances that are distributions, or sample sets drawn from distributions.

• Previous work devoted to machine learning tasks with distributional inputs has done so through pairwise kernel evaluations between pdfs (or sample sets).
• While such an approach is fine for smaller datasets, the computation of an $N \times N$ Gram matrix is prohibitive in large datasets.
• Recent scalable estimators that work over pdfs have done so only with kernels that use Euclidean metrics, like the $L_2$ distance.