An adaptive neighborhood choosing of the local sensitive discriminant analysis algorithm

摘要：

The curse of dimensionality is a problem of machine learning algorithm which is often encountered on study of high-dimensional data,while LSDA (Locality Sensitive Discriminant Analysis) can solve the problem of curse of dimensionality. However,LSDA can not fully reflect the requirements that the manifold learning for neighborhood,by using the adaptive neighborhood selection method to measure the neighborhood,it proposes an adaptive neighborhood choosing of the local sensitive discriminant analysis algorithm. Experimental results verify the effectiveness of the algorithm from the ORL and YALE face database.

关键词： Locality Sensitive Discriminant Analysis Manifold learning Neighborhood Choosing Face Recognition

作者: Gao weijun Bai wanrong Gong weijun

作者单位: School of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,China

会议类型: 国际会议

会议名称: 2011 International Conference on Opto-Electronics Engineering and Information Science(2011光电电子工程与信息科学国际会议 ICOEIS 2011)

会议地点: 西安

会议语种:英文

页码: 154-158

在线出版日期: 2011-12-23（万方平台首次上网日期，不代表论文的发表时间）