Reweighting Recognition Using Modified Kernel Principal Component Analysis via Manifold Learning
(0) Kernel Principal Component Analysis (KPCA) is a widely used technique in the dimension reduction, denoising and discovering nonlinear intrinsic dimensions of data set. In this paper we describe a reweighing kernel-based classification method for improving recognition problem. Firstly, we map the training samples to the feature space by non-linear transformation, and then perform principal component analysis(PCA) using the selected kernel function in the feature space, and get the linear representation of testing samples in the feature space. Secondly, by using the idea of reweighting, we select the similarity between testing sample and each training sample as the weight of reweighting, then take the final weight as the criteria of classification. The experimental results demonstrate that our method is more accurate than Support Vector Machine (SVM) classification method and Linear Discriminant Analysis (LDA) classification. In addition, the number of training samples that our method need is much smaller than some other methods.
Kernel Principal Component Analysis manifold learning Kernel function Reweighting
Xu Kejia Tan Zhiying Chen Bin
Chengdu Inst. of Computer Application, Chinese Academy of Sciences Chengdu, Sichuan Province, China
国际会议
武汉
英文
46-49
2010-12-05（万方平台首次上网日期，不代表论文的发表时间）