Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
405450 | Neural Networks | 2014 | 8 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Quanxue Gao, Jingjing Liu, Kai Cui, Hailin Zhang, Xiaogang Wang,