{"type":"ScholarlyArticle","title":"Deep Multi-Dimensional Classification with Pairwise Dimension-Specific Features","authors":[{"name":"Teng Huang","slug":"teng-huang","given":"Teng","family":"Huang","affiliation":"School of Computer Science and Engineering , Southeast University , Nanjing 210096 , China; Key Lab. of Computer Network and Information Integration ( Southeast University) , MOE , China"},{"name":"Bin-Bin Jia","slug":"bin-bin-jia","given":"Bin-Bin","family":"Jia","affiliation":"College of Electrical and Information Engineering , Lanzhou University of Technology , Lanzhou 730050 , China"},{"name":"Min-Ling Zhang","slug":"min-ling-zhang","given":"Min-Ling","family":"Zhang","affiliation":"School of Computer Science and Engineering , Southeast University , Nanjing 210096 , China; Key Lab. of Computer Network and Information Integration ( Southeast University) , MOE , China"}],"abstract":"In multi-dimensional classification (MDC), each instance is associated with multiple class variables characterizing the semantics of objects from different dimensions.  To consider the dependencies among class variables and the specific characteristics contained in different semantic dimensions, a novel deep MDC approach named PIST is proposed to jointly deal with the two issues via learning pairwise dimension-specific features. Specifically, PIST conducts pairwise grouping to model the dependencies between each pair of class variables, which are more reliable with limited training samples. For extracting pairwise dimension-specific features, PIST weights the feature embedding with a feature importance vector, which is learned via utilizing a global loss measurement based on intra-class and inter-class covariance.  Final prediction w.r.t. each dimension is determined by combining the joint probabilities related to this dimension. Comparative studies with eleven real-world MDC data sets clearly validate the effectiveness of the proposed approach.","doi":"10.24963/ijcai.2024/462","url":"https://ijcai.org/proceedings/2024/462","pdf":"https://ijcai.org/proceedings/2024/0462.pdf","markdown":"https://ijcai.org/proceedings/2024/462.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/462","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":462,"pages":{"first":4183,"last":4191},"keywords":["Machine Learning: ML: Multi-label learning","Machine Learning: ML: Classification"],"videos":[],"references":[{"position":1,"raw":"[Bielza et al., 2011] Concha Bielza, Guangdi Li, and Pedro Larrañaga. 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