{"type":"ScholarlyArticle","title":"Locality Adaptive Discriminant Analysis","authors":[{"name":"Xuelong Li","slug":"xuelong-li","given":"Xuelong","family":"Li","affiliation":"School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) , Northwestern Polytechnical University , Xi'an 710072 , Shaanxi , P. R. China"},{"name":"Mulin Chen","slug":"mulin-chen","given":"Mulin","family":"Chen","affiliation":"School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) , Northwestern Polytechnical University , Xi'an 710072 , Shaanxi , P. R. China"},{"name":"Feiping Nie","slug":"feiping-nie","given":"Feiping","family":"Nie","affiliation":"School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) , Northwestern Polytechnical University , Xi'an 710072 , Shaanxi , P. R. China"},{"name":"Qi Wang","slug":"qi-wang","given":"Qi","family":"Wang","affiliation":"School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) , Northwestern Polytechnical University , Xi'an 710072 , Shaanxi , P. R. China; Unmanned System Research Institute (USRI) , Northwestern Polytechnical University , Xi'an 710072 , Shaanxi , P. R. China Xuelong"}],"abstract":"Linear Discriminant Analysis (LDA) is a popular technique for supervised dimensionality reduction, and its performance is satisfying when dealing with Gaussian distributed data. However, the neglect of local data structure makes LDA inapplicable to many real-world situations. So some works focus on the discriminant analysis between neighbor points, which can be easily affected by the noise in the original data space. In this paper, we propose a new supervised dimensionality reduction method, Locality Adaptive Discriminant Analysis (LADA), to lean a representative subspace of the data. Compared to LDA and its variants, the proposed method has three salient advantages: (1) it finds the principle projection directions without imposing any assumption on the data distribution; (2) it’s able to exploit the local manifold structure of data in the desired subspace; (3) it exploits the points’ neighbor relationship automatically without introducing any additional parameter to be tuned. Performance on synthetic datasets and real-world benchmark datasets demonstrate the superiority of the proposed method.","doi":"10.24963/ijcai.2017/306","url":"https://ijcai.org/proceedings/2017/306","pdf":"https://ijcai.org/proceedings/2017/0306.pdf","markdown":"https://ijcai.org/proceedings/2017/306.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/306","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":306,"pages":{"first":2201,"last":2207},"keywords":["Machine Learning: Classification","Machine Learning: Semi-Supervised Learning","Machine Learning: Machine Learning","Machine Learning: Feature Selection/Construction"],"videos":[],"references":[{"position":1,"raw":"[Bressan and Vitria, 2003] Marco Bressan and Jordi Vitria. 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