Multidataset Independent Subspace Analysis with Application to Multimodal Fusion

TL;DR

This paper introduces MISA, a new method for combining multiple datasets to extract shared and unique features, enhancing multimodal data analysis especially in noisy or limited data scenarios.

Contribution

MISA unifies ICA, IVA, and ISA into a single framework using a novel subspace modeling approach with Kotz distribution and combinatorial optimization.

Findings

MISA effectively fuses multimodal brain imaging data.

It performs well in low SNR and sample-poor conditions.

Demonstrates improved feature extraction over existing methods.

Abstract

In the last two decades, unsupervised latent variable models---blind source separation (BSS) especially---have enjoyed a strong reputation for the interpretable features they produce. Seldom do these models combine the rich diversity of information available in multiple datasets. Multidatasets, on the other hand, yield joint solutions otherwise unavailable in isolation, with a potential for pivotal insights into complex systems. To take advantage of the complex multidimensional subspace structures that capture underlying modes of shared and unique variability across and within datasets, we present a direct, principled approach to multidataset combination. We design a new method called multidataset independent subspace analysis (MISA) that leverages joint information from multiple heterogeneous datasets in a flexible and synergistic fashion. Methodological innovations exploiting the Kotz distribution for subspace modeling in conjunction with a novel combinatorial optimization for evasion of local minima enable MISA to produce a robust generalization of independent component analysis (ICA), independent vector analysis (IVA), and independent subspace analysis (ISA) in a single unified model. We highlight the utility of MISA for multimodal information fusion, including sample-poor regimes and low signal-to-noise ratio scenarios, promoting novel applications in both unimodal and multimodal brain imaging data.