Principal Component Analysis for Interval-Valued Observations

Abstract : One feature of contemporary datasets is that instead of the single point value in the p-dimensional space p seen in classical data, the data may take interval values thus producing hypercubes in 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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Statistical Analysis and Data Mining, 2011, 4 (2), pp.229-246. <10.1002/sam.10118>


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Ahlame Douzal-Chouakria, Lynne Billard, Edwin Diday. Principal Component Analysis for Interval-Valued Observations. Statistical Analysis and Data Mining, 2011, 4 (2), pp.229-246. <10.1002/sam.10118>. <hal-00659996>

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