---
title: "Matrix Factorization with Scale-Invariant Parameters"
authors:
  - "Guangxiang Zeng"
  - "Hengshu Zhu"
  - "Qi Liu"
  - "Ping Luo"
  - "Enhong Chen"
  - "Tong Zhang"
doi: ""
url: "https://ijcai.org/proceedings/2015/564"
pdf: "https://ijcai.org/proceedings/2015/0564.pdf"
venue: "Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence"
year: 2015
pages: "4017-4023"
track: "Special Track on Machine Learning"
keywords:
---

# Matrix Factorization with Scale-Invariant Parameters

Guangxiang Zeng, Hengshu Zhu, Qi Liu, Ping Luo, Enhong Chen, Tong Zhang

*Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence*, pages 4017-4023.

## Abstract

Tuning hyper-parameters for large-scale matrix factorization (MF) is very time consuming and sometimes unacceptable. Intuitively, we want to tune hyper-parameters on small sub-matrix sample and then exploit them into the original large-scale matrix. However, most of existing MF methods are scale-variant, which means the optimal hyper-parameters usually change with the different scale of matrices. To this end, in this paper we propose a scale-invariant parametric MF method, where a set of scale-invariant parameters is defined for model complexity regularization. Therefore, the proposed method can free us from tuning hyper-parameters on large-scale matrix, and achieve a good performance in a more efficient way. Extensive experiments on real-world dataset clearly validate both the effectiveness and efficiency of our method.