Binary adaptive embeddings from order statistics of random projections
Machine Learning
2017-01-31 v1 Information Retrieval
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.
Cite
@article{arxiv.1701.08511,
title = {Binary adaptive embeddings from order statistics of random projections},
author = {Diego Valsesia and Enrico Magli},
journal= {arXiv preprint arXiv:1701.08511},
year = {2017}
}