Mutual interdependence analysis (MIA)

Authors:
Heiko Claussen;Justinian Rosca;Robert Damper
Affiliations:
Siemens Corporate Research Inc., Princeton, New Jersey and University of Southampton, School of Electronics and Computer Science, Southampton, UK;Siemens Corporate Research Inc., Princeton, New Jersey;University of Southampton, School of Electronics and Computer Science, Southampton, UK
Venue:
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Year:
2007

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Abstract

Functional Data Analysis (FDA) is used for datasets that are more meaningfully represented in the functional form. Functional principal component analysis, for instance, is used to extract a set of functions of maximum variance that can represent the data. In this paper, a method of Mutual Interdependence Analysis (MIA) is proposed that can extract an equally correlated function with a set of inputs. Formally, the MIA criterion defines the function whose mean variance of correlations with all inputs is minimized. The meaningfulness of the MIA extraction is proven on real data. In a simple text independent speaker verification example, MIA is used to extract a signature function per each speaker, and results in an equal error rate of 2.9 % in the set of 168 speakers.