The Correspondence Analysis Platform for Uncovering Deep Structure in Data and Information

摘要

We study two aspects of information semantics: (i) the collection of all relationships, (ii) tracking and spotting anomaly and change. The first is implemented by endowing all relevant information spaces with a Euclidean metric in a common projected space. The second is modelled by an induced ultrametric. A very general way to achieve a Euclidean embedding of different information spaces based on cross-tabulation counts (and from other input data formats) is provided by correspondence analysis. From there, the induced ultrametric that we are particularly interested in takes a sequential-e.g. temporal-ordering of the data into account. We employ such a perspective to look at narrative, 'the flow of thought and the flow of language' (Chafe). In application to policy decision making, we show how we can focus analysis in a small number of dimensions.