{"type":"ScholarlyArticle","title":"Deep Linear Coding for Fast Graph Clustering","authors":[{"name":"Ming Shao","slug":"ming-shao","given":"Ming","family":"Shao","affiliation":""},{"name":"Sheng Li","slug":"sheng-li-2","given":"Sheng","family":"Li","affiliation":""},{"name":"Zhengming Ding","slug":"zhengming-ding","given":"Zhengming","family":"Ding","affiliation":""},{"name":"Yun Fu","slug":"yun-fu","given":"Yun","family":"Fu","affiliation":""}],"abstract":"Clustering has been one of the most critical unsupervised learning techniques that has been widely applied in data mining problems. As one of its branches, graph clustering enjoys its popularity due to its appealing performance and strong theoretical supports. However, the eigen-decomposition problems involved are computationally expensive. In this paper, we propose a deep structure with a linear coder as the building block for fast graph clustering, called Deep Linear Coding (DLC). Different from conventional coding schemes, we jointly learn the feature transform function and discriminative codings, and guarantee that the learned codes are robust in spite of local distortions. In addition, we use the proposed linear coders as the building blocks to formulate a deep structure to further refine features in a layerwise fashion. Extensive experiments on clustering tasks demonstrate that our method performs well in terms of both time complexity and clustering accuracy. On a large-scale benchmark dataset (580K), our method runs 1500 times faster than the original spectral clustering.","doi":null,"url":"https://ijcai.org/proceedings/2015/534","pdf":"https://ijcai.org/proceedings/2015/0534.pdf","markdown":"https://ijcai.org/proceedings/2015/534.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/534","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":534,"pages":{"first":3798,"last":3803},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}