Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices

• URL: http://arxiv.org/abs/2009.02553v2
• Date: Thu, 17 Dec 2020 06:55:55 GMT
• Title: Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices
• Authors: Luo Luo, Cheng Chen, Guangzeng Xie, Haishan Ye
• Abstract summary: 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)
• Score: 23.22254890452548
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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.

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