{"type":"ScholarlyArticle","title":"Learning Canonical Correlations of Paired Tensor Sets Via Tensor-to-Vector Projection","authors":[{"name":"Haiping Lu","slug":"haiping-lu","given":"Haiping","family":"Lu","affiliation":""}],"abstract":"Canonical correlation analysis (CCA) is a useful technique for measuring relationship between two sets of vector data. For paired tensor data sets, we propose a multilinear CCA (MCCA) method. Unlike existing multilinear variations of CCA, MCCA extracts uncorrelated features under two architectures while maximizing paired correlations. Through a pair of tensor-to-vector projections, one architecture enforces zero-correlation within each set while the other enforces zero-correlation between different pairs of the two sets. We take a successive and iterative approach to solve the problem. Experiments on matching faces of different poses show that MCCA outperforms CCA and 2D-CCA, while using much fewer features. In addition, the fusion of two architectures leads to performance improvement, indicating complementary information.","doi":null,"url":"https://ijcai.org/proceedings/2013/226","pdf":"https://ijcai.org/proceedings/2013/0226.pdf","markdown":"https://ijcai.org/proceedings/2013/226.md","bibtex":"https://ijcai.org/proceedings/2013/bibtex/226","volume":{"slug":"2013","title":"Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2013","url":"https://ijcai.org/proceedings/2013/","year":2013,"location":"Beijing, China","dates":"3–9 August 2013","editors":"Francesca Rossi","isbn":"978-1-57735-633-2","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":226,"pages":{"first":1516,"last":1521},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}