教師資料查詢 | 類別: 期刊論文 | 教師: 吳漢銘 Han-ming Wu (瀏覽個人網頁)

標題:Kernel Sliced Inverse Regression with Applications to Classification
學年
學期
出版(發表)日期2008/09/01
作品名稱Kernel Sliced Inverse Regression with Applications to Classification
作品名稱(其他語言)
著者Wu, Han-Ming
單位淡江大學數學學系
出版者Philadelphia: Taylor & Francis Inc.
著錄名稱、卷期、頁數Journal of Computational and Graphical Statistics 17(3), pp.590-610
摘要Sliced inverse regression (SIR) was introduced by Li to find the effective dimension reduction directions for exploring the intrinsic structure of high-dimensional data. In this study, we propose a hybrid SIR method using a kernel machine which we call kernel SIR. The kernel mixtures result in the transformed data distribution being more Gaussian like and symmetric; providing more suitable conditions for performing SIR analysis. The proposed method can be regarded as a nonlinear extension of the SIR algorithm. We provide a theoretical description of the kernel SIR algorithm within the framework of reproducing kernel Hilbert space (RKHS). We also illustrate that kernel SIR performs better than several standard methods for discriminative, visualization, and regression purposes. We show how the features found with kernel SIR can be used for classification of microarray data and several other classification problems and compare the results with those obtained with several existing dimension reduction techniques. The results show that kernel SIR is a powerful nonlinear feature extractor for classification problems.
關鍵字Dimension reduction; Kernel machines; Reproducing kernel Hilbert space; Visualization
語言英文
ISSN1061-8600
期刊性質國外
收錄於SCI
產學合作
通訊作者
審稿制度
國別美國
公開徵稿
出版型式紙本
相關連結
SDGs
Google+ 推薦功能,讓全世界都能看到您的推薦!