{"type":"ScholarlyArticle","title":"Matrix Factorization with Scale-Invariant Parameters","authors":[{"name":"Guangxiang Zeng","slug":"guangxiang-zeng","given":"Guangxiang","family":"Zeng","affiliation":""},{"name":"Hengshu Zhu","slug":"hengshu-zhu","given":"Hengshu","family":"Zhu","affiliation":""},{"name":"Qi Liu","slug":"qi-liu","given":"Qi","family":"Liu","affiliation":""},{"name":"Ping Luo","slug":"ping-luo","given":"Ping","family":"Luo","affiliation":""},{"name":"Enhong Chen","slug":"enhong-chen","given":"Enhong","family":"Chen","affiliation":""},{"name":"Tong Zhang","slug":"tong-zhang","given":"Tong","family":"Zhang","affiliation":""}],"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.","doi":null,"url":"https://ijcai.org/proceedings/2015/564","pdf":"https://ijcai.org/proceedings/2015/0564.pdf","markdown":"https://ijcai.org/proceedings/2015/564.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/564","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Special Track on Machine Learning","track":"Special Track on Machine Learning","number":564,"pages":{"first":4017,"last":4023},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}