{"type":"ScholarlyArticle","title":"Instance-Wise Weighted Nonnegative Matrix Factorization for Aggregating Partitions with Locally Reliable Clusters","authors":[{"name":"Xiaodong Zheng","slug":"xiaodong-zheng","given":"Xiaodong","family":"Zheng","affiliation":""},{"name":"Shanfeng Zhu","slug":"shanfeng-zhu","given":"Shanfeng","family":"Zhu","affiliation":""},{"name":"Junning Gao","slug":"junning-gao","given":"Junning","family":"Gao","affiliation":""},{"name":"Hiroshi Mamitsuka","slug":"hiroshi-mamitsuka","given":"Hiroshi","family":"Mamitsuka","affiliation":""}],"abstract":"We address an ensemble clustering problem, where reliable clusters are locally embedded in given multiple partitions. We propose a new nonnegative matrix factorization (NMF)-based method, in which locally reliable clusters are explicitly considered by using instance-wise weights over clusters. Our method factorizes the input cluster assignment matrix into two matrices H and W, which are optimized by iteratively 1) updating H and W while keeping the weight matrix constant and 2) updating the weight matrix while keeping H and W constant, alternatively. The weights in the second step were updated by solving a convex problem, which makes our algorithm significantly faster than existing NMF-based ensemble clustering methods. We empirically proved that our method outperformed a lot of cutting-edge ensemble clustering methods by using a variety of datasets.","doi":null,"url":"https://ijcai.org/proceedings/2015/574","pdf":"https://ijcai.org/proceedings/2015/0574.pdf","markdown":"https://ijcai.org/proceedings/2015/574.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/574","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":574,"pages":{"first":4025,"last":4030},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}