PathFinder: Joint Decompositions of Linked Multimodal Datasets

Abstract:Low-rank matrix decompositions can uncover patterns and structure in data and have a number of different applications across many disciplines. Extensions to "joint" low-rank decompositions have been proposed to link datasets from different modalities. While these methods enable the discovery of common patterns across modalities, they require that all the multimodal data share one or more dimensions. We propose a new analysis method, PathFinder, that enables co-analysis of datasets that do not necessarily all share a dimension. The key insight is that as long as pairs or subgroups of matrices do share some dimension, and that there are one or more paths that link across the data matrices, a global joint decomposition can be sought out. This enables the joint estimation of common patterns across different modalities, species, or scales, where a one-to-one mapping across all data along some dimension is not necessarily available. We show that PathFinder is a general umbrella under which many matrix decomposition methods fall as special cases. It can be used to discover common patterns across disparate datasets and to make predictions for missing data or modalities.

Machine Learning (cs.LG); Image and Video Processing (eess.IV); Quantitative Methods (q-bio.QM); Machine Learning (stat.ML)

arXiv:2608.14951 [cs.LG]

(or arXiv:2608.14951v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.14951 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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