{"type":"ScholarlyArticle","title":"A Grassmannian Manifold Self-Attention Network for Signal Classification","authors":[{"name":"Rui Wang","slug":"rui-wang","given":"Rui","family":"Wang","affiliation":"School of Artificial Intelligence and Computer Science , Jiangnan University , Wuxi , China; Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence , Jiangnan University , Wuxi , China"},{"name":"Chen Hu","slug":"chen-hu","given":"Chen","family":"Hu","affiliation":"School of Artificial Intelligence and Computer Science , Jiangnan University , Wuxi , China; Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence , Jiangnan University , Wuxi , China"},{"name":"Ziheng Chen","slug":"ziheng-chen","given":"Ziheng","family":"Chen","affiliation":"Department of Information Engineering and Computer Science , University of Trento , Trento , Italy"},{"name":"Xiao-Jun Wu","slug":"xiao-jun-wu","given":"Xiao-Jun","family":"Wu","affiliation":"School of Artificial Intelligence and Computer Science , Jiangnan University , Wuxi , China; Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence , Jiangnan University , Wuxi , China"},{"name":"Xiaoning Song","slug":"xiaoning-song","given":"Xiaoning","family":"Song","affiliation":"School of Artificial Intelligence and Computer Science , Jiangnan University , Wuxi , China; Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence , Jiangnan University , Wuxi , China"}],"abstract":"In the community of artificial intelligence, significant progress has been made in encoding sequential data using deep learning techniques. Nevertheless, how to effectively mine useful information from channel dimensions remains a major challenge, as these features have a submanifold structure. Linear subspace, the basic element of the Grassmannian manifold, has proven to be an effective manifold-valued feature descriptor in statistical representation. Besides, the Euclidean self-attention mechanism has shown great success in capturing long-range relationships of data. Inspired by these facts, we extend the self-attention mechanism to the Grassmannian manifold. Our framework can effectively characterize the spatiotemporal fluctuations of sequential data encoded in the Grassmannian manifold. Extensive experimental results on three benchmarking datasets (a drone recognition dataset and two EEG signal classification datasets) demonstrate the superiority of our method over the state-of-the-art. The code and supplementary material for this work can be found at https://github.com/ChenHu-ML/GDLNet.","doi":"10.24963/ijcai.2024/564","url":"https://ijcai.org/proceedings/2024/564","pdf":"https://ijcai.org/proceedings/2024/0564.pdf","markdown":"https://ijcai.org/proceedings/2024/564.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/564","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":564,"pages":{"first":5099,"last":5107},"keywords":["Machine Learning: ML: Attention models","Machine Learning: ML: Geometric learning","Machine Learning: ML: Classification"],"videos":[],"references":[{"position":1,"raw":"[Absil et al., 2009] P-A Absil, Robert Mahony, and Rodolphe Sepulchre. 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