# Multidataset Independent Subspace Analysis with Application to Multimodal Fusion

arXiv: https://arxiv.org/abs/1911.04048 · HTML: https://mlsanity.com/paper/1911.04048/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2019.

## Built on by (most cited first, 4 of 4)

- [Fusing multimodal neuroimaging data with a variational autoencoder](https://mlsanity.com/paper/2105.01128/) (2021)
- [Copula-Linked Parallel ICA: A Method for Coupling Structural and Functional MRI brain Networks](https://mlsanity.com/paper/2410.19774/) (2024)
- [A 1/R Law for Kurtosis Contrast in Balanced Mixtures](https://mlsanity.com/paper/2602.22334/) (2026)
- [MSR-IVA: Masked Structural Residual Independent Vector Analysis for State-Aware Fusion of Structural MRI and Dynamic Functional Network Connectivity](https://mlsanity.com/paper/2608.24978/) (2026)

## Abstract (arXiv)

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.