Principal component analysis for interval-valued observations
Diday, Edwin; Douzal-Chouakria, Ahlame; Billard, Lynne (2011), Principal component analysis for interval-valued observations, Statistical Analysis and Data Mining, 4, 2, p. 229-246. http://dx.doi.org/10.1002/sam.10118
TypeArticle accepté pour publication ou publié
External document linkhttp://hal.archives-ouvertes.fr/hal-00361053
Journal nameStatistical Analysis and Data Mining
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Abstract (EN)One feature of contemporary datasets is that instead of the single point value in the p-dimensional space ℜp seen in classical data, the data may take interval values thus producing hypercubes in ℜp. 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.
Subjects / Keywordsinertia; correlations; vertex contributions; vertices principal components
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