Data-Dependent Kernel Discriminant Analysis for Feature Extraction and Classification

摘要：

Subspace analysis is an effective technique for feature extraction, which aims at finding a low-dimensional space of high-dimensional data. In this paper, a novel subspace analysis method based on Data-Dependent Kernel Discriminant Analysis (DDKDA) is proposed for dimension reduction. The procedure of DDKDA contains two stages: one is to find the optimal combination coefficients by solving a constrained optimization function which transformed to an eigenvalue problem; other is to implement KDA under the optimal data-dependent kernel with Fisher criterion. DDKDA is more adaptive to the input data than KDA owing to the optimization of projection from input space to feature space with the data-dependent kernel, which enhances the performance of KDA. Experiments on the ORL and Yale face databases demonstrate the good performance of the proposed algorithm.

关键词： Data-Dependent Kernel Discriminant Analyis Kernel Discriminant Analysi Kernel Method.

作者: Jun-Bao Li Jeng-Shyang Pan Zhe-Ming Lu Bin-Yih Liao

作者单位: Department of Automatic Test and Control Harbin Institute of Technology, Harbin, China Department of Electronic Engineering National Kaohsiung University of Applied Sciences, Kaohsiung,Ta Visual Information Analysis and Processing Research Center Harbin Institute of Technology Shenzhen G Department of Electronic Engineering National Kaohsiung University of Applied Sciences, Kaohsiung, T

会议类型: 国际会议

会议名称: 2006 IEEE International Conference on Information Acquisition

会议地点: 山东威海

会议语种:英文

页码: 1263-1268

在线出版日期: 2006-08-20（万方平台首次上网日期，不代表论文的发表时间）