期刊刊名：管理科學與統計決策 　 卷期：7卷1期

篇名出版日期：2010年3月1日

作者：ZunXiong Liu,LiHui Zeng

語言：English

關鍵字：LDA,linear regression,RLS-LDA

被點閱次數：0次

閱讀時間：0sec

摘要：
Linear Discriminant 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 it’s the RLS-LDA based on the elastic net that is better than others.

　　　　　[全文下載]