Generalized mutual interdependence analysis

The mean of a data set is one trivial representation of data from one class. Recently, mutual interdependence analysis (MIA) has been successfully used to extract more involved representations, or ldquomutual featuresrdquo, accounting for samples in the class. For example a mutual feature is a speaker signature under varying channel conditions or a face signature under varying illumination conditions. A mutual representation is a linear regression that is equally correlated with all samples of the input class. We present the MIA optimization criterion from the perspectives of regression, canonical correlation analysis and Bayesian estimation. This allows us to state and solve the above criterion concisely, to contrast the MIA solution to the sample mean, and to infer other properties of its closed form, unique solution under various statistical assumptions. We define a generalized MIA solution (GMIA) and apply MIA and GMIA in a text-independent speaker verification task using the NTIMIT database. Both methods show competitive performance with equal-error-rates of 7.5 % and 6.5 % respectively over 630 speakers.