Mutual Interdependence Analysis (MIA)

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.

• Type: Conference
• Year: 2007
• Where: ICA
• Authors: Heiko Claussen, Justinian Rosca, Robert I. Damper
• Keywords: Functional Data Analysis, Functional Principal Component, ICA 2007, Mutual Interdependence Analysis, Signal Processing