{"type":"ScholarlyArticle","title":"Self-Representative Manifold Concept Factorization with Adaptive Neighbors for Clustering","authors":[{"name":"Sihan Ma","slug":"sihan-ma","given":"Sihan","family":"Ma","affiliation":"School of Computer , Wuhan University"},{"name":"Lefei Zhang","slug":"lefei-zhang","given":"Lefei","family":"Zhang","affiliation":"School of Computer , Wuhan University"},{"name":"Wenbin Hu","slug":"wenbin-hu","given":"Wenbin","family":"Hu","affiliation":"School of Computer , Wuhan University"},{"name":"Yipeng Zhang","slug":"yipeng-zhang","given":"Yipeng","family":"Zhang","affiliation":"School of Computer , Wuhan University"},{"name":"Jia Wu","slug":"jia-wu","given":"Jia","family":"Wu","affiliation":"Department of Computing , Macquarie University"},{"name":"Xuelong Li","slug":"xuelong-li","given":"Xuelong","family":"Li","affiliation":"Center for OPTIMAL , Xi'an Institute of Optics and Precision Mechanics , CAS"}],"abstract":"Matrix Factorization based methods, e.g., the Concept Factorization (CF) and Nonnegative Matrix Factorization (NMF), have been proved to be efficient and effective for data clustering tasks. In recent years, various graph extensions of CF and NMF have been proposed to explore intrinsic geometrical structure of data for the purpose of better clustering performance. However, many methods build the affinity matrix used in the manifold structure directly based on the input data. Therefore, the clustering results are highly sensitive to the input data. To further improve the clustering performance, we propose a novel manifold concept factorization model with adaptive neighbor structure to learn a better affinity matrix and clustering indicator matrix at the same time. Technically, the proposed model constructs the affinity matrix by assigning the adaptive and optimal neighbors to each point based on the local distance of the learned new representation of the original data with itself as a dictionary. Our experimental results present superior performance over the state-of-the-art alternatives on numerous datasets.","doi":"10.24963/ijcai.2018/352","url":"https://ijcai.org/proceedings/2018/352","pdf":"https://ijcai.org/proceedings/2018/0352.pdf","markdown":"https://ijcai.org/proceedings/2018/352.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/352","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning","track":"Main track","number":352,"pages":{"first":2539,"last":2545},"keywords":["Machine Learning Applications: Applications of Unsupervised Learning","Machine Learning: Data Mining","Machine Learning: Dimensionality Reduction and Manifold Learning","Machine Learning: Clustering","Computer Vision: Statistical Methods and Machine Learning"],"videos":[],"references":[{"position":1,"raw":"[Belkin and Niyogi, 2002] Mikhail Belkin and Partha Niyogi. 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