Non-orthogonal constrained independent vector analysis: Application to data fusion

Abstract

The existence of complementary information across multiple sensors has driven the proliferation of multivariate datasets. Exploitation of this common information, while minimizing the assumptions imposed on the data has led to the popularity of data-driven methods. Independent vector analysis (IVA), in particular, provides a flexible and effective approach for the fusion of multivariate data. In many practical applications, important prior information about the data exists and incorporating this information into the IVA model is expected to yield improved separation performance. In this paper, we propose a general formulation for non-orthogonal constrained IVA (C-IVA) framework that can incorporate prior information about either the sources or the mixing coefficients into the IVA cost function. A powerful decoupling method is the major enabling factor in this task. We demonstrate the improved performance of C-IVA over the unconstrained IVA model using both simulated as well as real medical imaging data.