关键词: 隐空间, 直接鉴别分析, 加权系数, 特征抽取

Abstract: In this paper，a novel nonlinear feature extraction method，parameterized direct linear discriminant analysis in hidden space（PD-LDA-HS） is proposed.The main idea of PD-LDA-HS is that a kernel function is used to nonlinearly map the original input space into a hidden space，in which problems such as that within-class scatter is always singular are conducted by using PD-LDA in feature extraction.Different from the existing kernel feature extraction methods，the kernel function used in the proposed one is not required to satisfy Mercer’s theorem so that they can be chosen from a wide range.It is more important that due to the adoption of PD-LDA in hidden space，not only the advantages of PD-LDA are preserved，but also nonlinear feature of samples is effectively extracted.A more rational weight coefficient matrix is proposed to improve the classification performance.The experimental results based on the sub-set of FERET face database show the effectiveness of PD-LDA-HS.

Key words: hidden space, direct Linear Discriminant Analysis（LDA）, weight coefficient, feature extraction