ICML 2018oral31 citations
Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices
Abstract
Given a large matrix $A\in\real^{n\times d}$, we consider the problem of computing a sketch matrix $B\in\real^{\ell\times d}$ which is significantly smaller than but still well approximates $A$. We are interested in minimizing the
BibTeX
@InProceedings{pmlr-v80-huang18a,
title = {Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices},
author = {Huang, Zengfeng},
booktitle = {Proceedings of the 35th International Conference on Machine Learning},
pages = {2048--2057},
year = {2018},
editor = {Dy, Jennifer and Krause, Andreas},
volume = {80},
series = {Proceedings of Machine Learning Research},
month = {10--15 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v80/huang18a/huang18a.pdf},
url = {https://proceedings.mlr.press/v80/huang18a.html},
abstract = {Given a large matrix $A\in\real^{n\times d}$, we consider the problem of computing a sketch matrix $B\in\real^{\ell\times d}$ which is significantly smaller than but still well approximates $A$. We are interested in minimizing the