ICA 2007

ICA 2007
7th International Conference on
Independent Component Analysis
and Signal Separation

London, UK        9 - 12 September 2007

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Paper No: 137

Mutual Interdependence Analysis (MIA)

Author(s): Heiko Claussen, Justinian Rosca, Robert Damper

Abstract

Functional Data Analysis (FDA) is used for datasets that are more meaningfully represented in the form of functions. 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.

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