Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices
Abstract
We study the streaming model for approximate matrix multiplication (AMM). We are interested in the scenario that the algorithm can only take one pass over the data with limited memory. The state-of-the-art deterministic sketching algorithm for streaming AMM is the co-occurring directions (COD), which has much smaller approximation errors than randomized algorithms and outperforms other deterministic sketching methods empirically. In this paper, we provide a tighter error bound for COD whose leading term considers the potential approximate low-rank structure and the correlation of input matrices. We prove COD is space optimal with respect to our improved error bound. We also propose a variant of COD for sparse matrices with theoretical guarantees. The experiments on real-world sparse datasets show that the proposed algorithm is more efficient than baseline methods.
Cite
Text
Luo et al. "Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I10.17065Markdown
[Luo et al. "Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/luo2021aaai-revisiting/) doi:10.1609/AAAI.V35I10.17065BibTeX
@inproceedings{luo2021aaai-revisiting,
title = {{Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices}},
author = {Luo, Luo and Chen, Cheng and Xie, Guangzeng and Ye, Haishan},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2021},
pages = {8793-8800},
doi = {10.1609/AAAI.V35I10.17065},
url = {https://mlanthology.org/aaai/2021/luo2021aaai-revisiting/}
}