淡江大學機構典藏:Item 987654321/69183
English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 62819/95882 (66%)
造访人次 : 3998117      在线人数 : 667
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜寻范围 查询小技巧：
  • 您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果
  • 若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据
    •  进阶搜寻


    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/69183


    题名: Curve Data Classification via Functional Principal Component Analysis
    作者: Li, Pai-ling;Wang, Che-chiu
    贡献者: 淡江大學統計學系
    关键词: Classification;Functional data analysis;Functional principal component analysis;Mass spectrometry;Proteomics
    日期: 2010-12
    上传时间: 2011-10-23 16:31:41 (UTC+8)
    出版者: 臺北市: Airiti Press
    摘要: We propose a best predicted curve classification (BPCC) criterion for classifying the curve data. The data are viewed as realizations of a mixture of stochastic processes and each subprocess corresponds to a known class. Under the assumption that all the groups have different mean functions and eigenspaces, an observed curve is classified into the best predicted class by minimizing the distance between the observed and predicted curves via subspace projection among all classes based on the functional principal component analysis (FPCA) model. The BPCC approach accounts for both the means and the modes of variation differentials among classes while other classical functional classification methods consider the differences in mean functions only. Practical performance of the proposed method is demonstrated through simulation studies and a real data example of matrix assisted laser desorption (MALDI) mass spectrometry (MS) data. The proposed method is also compared with other multivariate and functional classification approaches. Overall, the BPCC method outperforms the others when the mean functions and the eigenspaces among classes are significantly distinct. For classifying the MALDI MS data, we found that functional classification methods perform better than multivariate data approaches, and the dimension reduction via FPCA is advantageous to improving the accuracy of classification.
    關聯: International Journal of Intelligent Technologies and Applied Statistics 3(4), pp.383-399
    DOI: 10.6148/IJITAS.2010.0304.02
    显示于类别:[統計學系暨研究所] 期刊論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    Curve Data Classification via Functional Principal Component Analysis.pdf2656KbAdobe PDF55检视/开启
    index.html0KbHTML40检视/开启

    在機構典藏中所有的数据项都受到原著作权保护.

    TAIR相关文章

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