淡江大學機構典藏:Item 987654321/50391
English  |  正體中文  |  简体中文  |  Items with full text/Total items : 58649/92364 (63%)
Visitors : 571000      Online Users : 112
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/50391


    Title: Kernel Sliced Inverse Regression with Applications to Classification
    Authors: Wu, Han-Ming
    Contributors: 淡江大學數學學系
    Keywords: Dimension reduction;Kernel machines;Reproducing kernel Hilbert space;Visualization
    Date: 2008-09
    Issue Date: 2010-08-09 16:42:41 (UTC+8)
    Publisher: Philadelphia: Taylor & Francis Inc.
    Abstract: 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.
    Relation: Journal of Computational and Graphical Statistics 17(3), pp.590-610
    DOI: 10.1198/106186008X345161
    Appears in Collections:[Graduate Institute & Department of Mathematics] Journal Article

    Files in This Item:

    File Description SizeFormat
    1061-8600_17(3)p590-610.pdf917KbAdobe PDF352View/Open
    index.html0KbHTML62View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback