NeurIPS 2017poster13 citations
Simple strategies for recovering inner products from coarsely quantized random projections
Abstract
Random projections have been increasingly adopted for a diverse set of tasks in machine learning involving dimensionality reduction. One specific line of research on this topic has investigated the use of quantization subsequent to projection with the aim of additional data compression. Motivated by applications in nearest neighbor search and linear learning, we revisit the problem of recovering inner products (respectively cosine similarities) in such setting. We show that even under coarse scalar quantization with 3 to 5 bits per projection, the loss in accuracy tends to range from
BibTeX
@inproceedings{NIPS2017_ea159dc9,
author = {Li, Ping and Slawski, Martin},
booktitle = {Advances in Neural Information Processing Systems},
editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Simple strategies for recovering inner products from coarsely quantized random projections},
url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/ea159dc9788ffac311592613b7f71fbb-Paper.pdf},
volume = {30},
year = {2017}
}