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政大機構典藏 > 商學院 > 統計學系 > 專書/專書篇章 > Item 140.119/139838

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/139838

Title:	Dimension reduction and visualization of symbolic interval-valued data using sliced inverse regression
Authors:	吳漢銘 Wu, Han-Ming Kao, Chiun-How Chen, Chun-houh
Contributors:	統計系
Keywords:	data visualization;dimension reduction;distributional approaches;interval-valued data;simulation studies;sliced inverse regression method;symbolic covariance matrix;symbolic-numerical-symbolic approaches
Date:	2020-01
Issue Date:	2022-04-12
Abstract:	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.
Relation:	Advances in Data Science: Symbolic, Complex and Network Data, John Wiley & Sons, Inc., pp.49-78
Data Type:	book/chapter
DOI 連結:	https://doi.org/10.1002/9781119695110.ch3
DOI:	10.1002/9781119695110.ch3
Appears in Collections:	[統計學系] 專書/專書篇章

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