NeurIPS 2019oral26 citations
Understanding Sparse JL for Feature Hashing
Abstract
Feature hashing and other random projection schemes are commonly used to reduce the dimensionality of feature vectors. The goal is to efficiently project a high-dimensional feature vector living in R^n into a much lower-dimensional space R^m, while approximately preserving Euclidean norm. These schemes can be constructed using sparse random projections, for example using a sparse Johnson-Lindenstrauss (JL) transform. A line of work introduced by Weinberger et. al (ICML '09) analyzes the accuracy of sparse JL with sparsity 1 on feature vectors with small l
BibTeX
@inproceedings{NEURIPS2019_502cc2c9,
author = {Jagadeesan, Meena},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Understanding Sparse JL for Feature Hashing},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/502cc2c94be1a7c4ca7ef25b8b50bc04-Paper.pdf},
volume = {32},
year = {2019}
}