## Nearest neighbor search for general symmetric norms via embeddings into product spaces

Ilya Razenshteyn

Massachusetts Institute of Technology

February 13, 2017

I will show a new efficient approximate nearest neighbor search (ANN) algorithm over an arbitrary high-dimensional *symmetric* norm. Traditionally, the ANN problem in high dimensions has been studied over the $\ell_1$ and $\ell_2$ distances with a few exceptions. Thus, the new result can be seen as a (modest) step towards a "unified theory" of similarity search.