---
title: "A Scalable Approach to Column-Based Low-Rank Matrix Approximation"
authors:
  - "Yifan Pi"
  - "Haoruo Peng"
  - "Shuchang Zhou"
  - "Zhihua Zhang"
doi: ""
url: "https://ijcai.org/proceedings/2013/238"
pdf: "https://ijcai.org/proceedings/2013/0238.pdf"
venue: "Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence"
year: 2013
pages: "1600-1605"
track: ""
keywords:
---

# A Scalable Approach to Column-Based Low-Rank Matrix Approximation

Yifan Pi, Haoruo Peng, Shuchang Zhou, Zhihua Zhang

*Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence*, pages 1600-1605.

## Abstract

In this paper, we address the column-based low-rank matrix approximation problem using a novel parallel approach. Our approach is based on the divide-and-combine idea. We first perform column selection on submatrices of an original data matrix in parallel, and then combine the selected columns into the final output. Our approach enjoys a theoretical relative-error upper bound. In addition, our column-based low-rank approximation partitions data in a deterministic way and makes no assumptions about matrix coherence. Compared with other traditional methods, our approach is scalable on large-scale matrices. Finally, experiments on both simulated and real world data show that our approach is both efficient and effective.