Regularized Least Squares LDA and Its Application in Text Classification

摘要：

Linear Diseriminant Analysis(LDA)is a well-known technique for dimensionality reduction and classification, while the classical LDA formulation fails when the total scatter matrix is singular, encountered usually in undersampled problems. In this paper, regularized Least Squares LDA (RLS-LDA) based on the elastic net, is proposed to handle the problems, and the resulting models are robust and sparse. Firstly, the theories about linear regression and regularization are explored, and the equivalence relationship between the least squares formulation and LDA for multi-class classifications under a mild condition is summarized.Secondly, the construction of RLS-LDA is presented. Performance evaluations of these approaches are conducted on benchmark collection of text documents. Results demonstrate the effectiveness of the proposed RLS-LDA and its the RLS-LDA based on the elastic net that is better than others.

关键词： LDA linear regression RLS-LDA

作者: ZunXiong Liu LiHui Zeng

作者单位: School of Information Engineering East China Jiaotong University Nanchang, China

会议类型: 国际会议

会议名称: The 10th International Conference on Intelligent Technologies(第十届智慧科技国际会议 InTech09)

会议地点: 桂林

会议语种:英文

页码: 206-210

在线出版日期: 2009-12-12（万方平台首次上网日期，不代表论文的发表时间）