主成分分析
区间(图论)
超立方体
计算机科学
数据挖掘
模式识别(心理学)
特征(语言学)
算法
顶点(图论)
区间数据
数学
人工智能
理论计算机科学
图形
度量(数据仓库)
组合数学
语言学
哲学
并行计算
作者
Ahlame Douzal-Chouakria,L. Billard,Edwin Diday
摘要
Abstract One feature of contemporary datasets is that instead of the single point value in the p ‐dimensional space ℜ p seen in classical data, the data may take interval values thus producing hypercubes in ℜ p . This paper studies the vertices principal components methodology for interval‐valued data; and provides enhancements to allow for so‐called ‘trivial’ intervals, and generalized weight functions. It also introduces the concept of vertex contributions to the underlying principal components, a concept not possible for classical data, but one which provides a visualization method that further aids in the interpretation of the methodology. The method is illustrated in a dataset using measurements of facial characteristics obtained from a study of face recognition patterns for surveillance purposes. A comparison with analyses in which classical surrogates replace the intervals, shows how the symbolic analysis gives more informative conclusions. A second example illustrates how the method can be applied even when the number of parameters exceeds the number of observations, as well as how uncertainty data can be accommodated. © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 4: 229–246 2011
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