# Binary adaptive embeddings from order statistics of random projections

## TL;DR

This paper introduces a binary embedding method based on order statistics of random projections, tailored for signals correlated with a reference, enhancing classification performance in low-dimensional spaces.

## Contribution

It proposes a novel adaptive binary embedding technique utilizing order statistics, improving upon existing methods for correlated signals.

## Key findings

- Enhanced classification accuracy in reduced-dimensionality space
- Analytical characterization of the embedding's properties
- Demonstrated improved performance over traditional embeddings

## Abstract

We use some of the largest order statistics of the random projections of a reference signal to construct a binary embedding that is adapted to signals correlated with such signal. The embedding is characterized from the analytical standpoint and shown to provide improved performance on tasks such as classification in a reduced-dimensionality space.