摘要
提出一种线性特征提取方法——类别非局保留投影.并进行核扩张,称为基于核的类别非局保留投影.基于非局保留投影特征提取方法,类别非局保留投影采用类间信息指导特征提取,同时考虑样本的关系信息和类别信息,并通过核技巧实现原输入空间的非线性判别.通过对yeast和NCI基因表达数据进行特征提取,对文中方法进行测试和评价.实验结果表明,该方法能获得较高的识别率.
A feature extraction method is proposed, namely class-wise non-locality preserving projection （CNLPP）. The kernelized counterpart of CNLPP linear feature extractor is also established. Based on the linear feature extractor-non-locality preserving projection （NLPP）, CNLPP utilizes between-class information to guide the procedure of feature extraction. CNLPP takes both the relation information and the class information into account. A kernel version of CNLPP, namely Kernel based CNLPP （KCNLPP）, is developed by applying the kernel trick to CNLPP to enhance its performance on nonlinear feature extraction. Experiments on yeast gene expression data and NCI gene expression data are performed to test and evaluate the performance of the proposed algorithm, and the results show that KCNLPP achieves relatively high recognition accuracy.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2009年第5期769-773,共5页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金资助项目(No.60371044
60574039
60533010)
关键词
特征提取
非局保留投影
降维
基因表达数据
Feature Extraction, Non-Locality Preserving Projection, Dimensionality Reduction, Gene Expression Data
