title: Stable locality sensitive discriminant analysis for image recognition.
abstract: Locality Sensitive Discriminant Analysis (LSDA) is one of the prevalent discriminant approaches based on manifold learning for dimensionality reduction. However, LSDA ignores the intra-class variation that characterizes the diversity of data, resulting in unstableness of the intra-class geometrical structure representation and not good enough performance of the algorithm. In this paper, a novel approach is proposed, namely stable locality sensitive discriminant analysis (SLSDA), for dimensionality reduction. SLSDA constructs an adjacency graph to model the diversity of data and then integrates it in the objective function of LSDA. Experimental results in five databases show the effectiveness of the proposed approach.
authors.affiliation: State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, China. Electronic address: qxgao@xidian.edu.cn.
authors.forename: Quanxue
authors.initials: Q
authors.lastname: Gao
authors.affiliation: State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, China.
authors.forename: Jingjing
authors.initials: J
authors.lastname: Liu
authors.affiliation: State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, China.
authors.forename: Kai
authors.initials: K
authors.lastname: Cui
authors.affiliation: State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, China.
authors.forename: Hailin
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong.
authors.forename: Xiaogang
authors.initials: X
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D016002:Discriminant Analysis; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*; D021641:Recognition, Psychology
keywords: Dimensionality reduction; Diversity; Manifold learning; Similarity