Multidataset independent subspace analysis extends independent vector analysis

• Main Authors: Silva, Rogers F., Plis, Sergey M., Adali, Tulay, Calhoun, Vince D.
• Format: Conference Proceeding
• Language: English
• Subjects: Cost function; Data models; Educational institutions; ICA; ISA; IVA; Joints; Manganese; MICA; MISA; multidataset; multimodal; Principal component analysis; Vectors
• ISSN: 1522-4880; 2381-8549
• DOI: 10.1109/ICIP.2014.7025579

Despite its multivariate nature, independent component analysis (ICA) is generally limited to univariate latents in the sense that each latent component is a scalar process. Independent subspace analysis (ISA), or multidimensional ICA (MICA), is a generalization of ICA which identifies latent independent vector components instead. While ISA/MICA considers multidimensional latent components within a single dataset, our work specifically considers the case of multiple datasets. Independent vector analysis (IVA) is a related technique that also considers multiple datasets explicitly but with a fixed and constrained model. Here, we first show that 1) ISA/MICA naturally extends to the case of multiple datasets (which we call MISA), and that 2) IVA is a special case of this extension. Then we develop an algorithm for MISA and demonstrate its performance on both IVA- and MISA-type problems. The benefit of these extensions is that the vector sources (or subspaces) capture higher order statistical dependence across datasets while retaining independence between subspaces. This is a promising model that can explore complex latent relations across multiple datasets and help identify novel biological traits for intricate mental illnesses such as schizophrenia.