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    题名: Dimension reduction and visualization of symbolic interval-valued data using sliced inverse regression
    作者: 吳漢銘
    Wu, Han-Ming
    Kao, Chiun-How
    Chen, Chun-houh
    贡献者: 統計系
    关键词: data visualization;dimension reduction;distributional approaches;interval-valued data;simulation studies;sliced inverse regression method;symbolic covariance matrix;symbolic-numerical-symbolic approaches
    日期: 2020-01
    上传时间: 2022-04-12
    摘要: Sliced inverse regression (SIR) is a popular slice-based sufficient dimension reduction technique for exploring the intrinsic structure of high-dimensional data. A main goal of dimension reduction is data visualization. This chapter reviews the extension of principal component analysis (PCA) to the interval-valued data, followed by a brief description of the classic SIR. It considers different families of symbolic-numerical-symbolic approaches to extend SIR to the interval-valued data. The chapter evaluates the implemented interval SIR methods and compare the results with those of interval PCA for low-dimensional discriminative and visualization purposes by means of simulation studies. The analysis of interval-valued data usually serves as the basic principle for analyzing other types of symbolic data, such as multi-valued data, modal-valued data, and modal multi-valued data. The advantage of the distributional approaches is that the resulting symbolic covariance matrix fully utilizes all the information in the data.
    關聯: Advances in Data Science: Symbolic, Complex and Network Data, John Wiley & Sons, Inc., pp.49-78
    数据类型: book/chapter
    DOI 連結: https://doi.org/10.1002/9781119695110.ch3
    DOI: 10.1002/9781119695110.ch3
    显示于类别:[統計學系] 專書/專書篇章

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