{"type":"ScholarlyArticle","title":"Symmetric Non-negative Latent Factor Models for Undirected Large Networks","authors":[{"name":"Xin Luo","slug":"xin-luo","given":"Xin","family":"Luo","affiliation":""},{"name":"Ming-Sheng Shang","slug":"ming-sheng-shang","given":"Ming-Sheng","family":"Shang","affiliation":""}],"abstract":"Undirected, high dimensional and sparse networks are frequently encountered in industrial applications. They contain rich knowledge regarding various useful patterns. Non-negative latent factor (NLF) models have proven to be effective and efficient in acquiring useful knowledge from asymmetric networks. However, they cannot correctly describe the symmetry of an undirected network. For addressing this issue, this work analyzes the NLF extraction processes on asymmetric and symmetric matrices respectively, thereby innovatively achieving the symmetric and non-negative latent factor (SNLF) models for undirected, high dimensional and sparse networks. The proposed SNLF models are equipped with a) high efficiency, b) non-negativity, and c) symmetry. Experimental results on real networks show that they are able to a) represent the symmetry of the target network rigorously; b) maintain the non-negativity of resulting latent factors; and c) achieve high computational efficiency when performing data analysis tasks as missing data estimation.","doi":"10.24963/ijcai.2017/339","url":"https://ijcai.org/proceedings/2017/339","pdf":"https://ijcai.org/proceedings/2017/0339.pdf","markdown":"https://ijcai.org/proceedings/2017/339.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/339","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":339,"pages":{"first":2435,"last":2442},"keywords":["Machine Learning: Feature Selection/Construction","Knowledge Representation, Reasoning, and Logic: Preference modelling and preference-based reasoning","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Adomavicius and Tuzhilin, 2005] Gediminas Adomavicius and Alexander Tuzhilin, \"Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions,\" IEEE Trans. on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734-749, 2005.","title":"Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions","authors":"Tuzhilin Adomavicius","year":"2005","venue":"IEEE Trans. on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Borhani et al., 2016] Reza Borhani, Jeremy Watt, and Aggelos Katsaggelos, \"Fast and Effective Algorithms for Symmetric Nonnegative Matrix Factorization,\" arXiv:1609.05342, 2016.","title":"Fast and Effective Algorithms for Symmetric Nonnegative Matrix Factorization","authors":"Reza Borhani, Jeremy Watt, Aggelos Katsaggelos","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Davis and Hu, 2011] Timothy Davis and Yifan Hu, \"The University of Florida Sparse Matrix Collection,\" ACM Transactions on Mathematical Software, vol. 38, no. 1, pp. 1-25, 2011.","title":"The University of Florida Sparse Matrix Collection","authors":"Davis, Hu, Timothy Davis, Yifan Hu","year":"2011","venue":"ACM Transactions on Mathematical Software","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Gao et al., 2016] Bin Gao, Wai-Lok Woo, Guiyun Tian, and Hong Zhang, \"Unsupervised Diagnostic and Monitoring of Defects Using Waveguide Imaging With Adaptive Sparse Representation,\" IEEE Trans. on Industrial Informatics, vol. 12, no. 1, pp. 405 -416, 2016.","title":"Unsupervised Diagnostic and Monitoring of Defects Using Waveguide Imaging With Adaptive Sparse Representation","authors":"Gao","year":"2016","venue":"IEEE Trans. on Industrial Informatics","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Ghahramani, 2015] Z. 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