{"type":"ScholarlyArticle","title":"A Convengent Solution to Tensor Subspace Learning","authors":[{"name":"Huan Wang","slug":"huan-wang","given":"Huan","family":"Wang","affiliation":""},{"name":"Shuicheng Yan","slug":"shuicheng-yan","given":"Shuicheng","family":"Yan","affiliation":""},{"name":"Thomas S. Huang","slug":"thomas-s-huang","given":"Thomas S.","family":"Huang","affiliation":""},{"name":"Xiaoou Tang","slug":"xiaoou-tang","given":"Xiaoou","family":"Tang","affiliation":""}],"abstract":"Recently, substantial efforts have been devoted to the subspace learning techniques based on tensor representation, such as 2DLDA, DATER and Tensor Subspace Analysis (TSA). In this context, a vital yet unsolved problem is that the computational convergency of these iterative algorithms is not guaranteed. In this work, we present a novel solution procedure for general tensor-based subspace learning, followed by a detailed convergency proof of the solution projection matrices and the objective function value. Extensive experiments on real-world databases verify the high convergence speed of the proposed procedure, as well as its superiority in classification capability over traditional solution procedures.","doi":null,"url":"https://ijcai.org/proceedings/2007/100","pdf":"https://ijcai.org/proceedings/2007/0100.pdf","markdown":"https://ijcai.org/proceedings/2007/100.md","bibtex":"https://ijcai.org/proceedings/2007/bibtex/100","volume":{"slug":"2007","title":"Proceedings of the Twentieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2007","url":"https://ijcai.org/proceedings/2007/","year":2007,"location":"Hyderabad, India","dates":"","editors":"Ramon López de Mántaras","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":100,"pages":{"first":629,"last":633},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}