Multidataset Independent Subspace Analysis with Application to Multimodal Fusion

Simple item page
dc.contributor.authorSilva, Rogers F.
dc.contributor.authorPlis, Sergey M.
dc.contributor.authorAdali, Tulay
dc.contributor.authorPattichis, Marios S.
dc.contributor.authorCalhoun, Vince D.
dc.date.accessioned2020-01-27T15:32:04Z
dc.date.available2020-01-27T15:32:04Z
dc.date.issued2019-11-11
dc.description.abstractIn 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.en_US
dc.description.urihttps://arxiv.org/abs/1911.04048en_US
dc.format.extent29 pagesen_US
dc.genrejournal articles preprintsen_US
dc.identifierdoi:10.13016/m2n0en-eeg3
dc.identifier.citationSilva, Rogers F.; Plis, Sergey M.; Adali, Tulay; Pattichis, Marios S.; Calhoun, Vince D.; Multidataset Independent Subspace Analysis with Application to Multimodal Fusion; Machine Learning (2019); https://arxiv.org/abs/1911.04048en_US
dc.identifier.urihttp://hdl.handle.net/11603/17057
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectBSSen_US
dc.subjectMISAen_US
dc.subjectmultidataseten_US
dc.subjectfusionen_US
dc.subjectICAen_US
dc.subjectISAen_US
dc.subjectIVAen_US
dc.subjectsubspaceen_US
dc.subjectunimodalen_US
dc.subjectmultimodalityen_US
dc.subjectmultiset data analysisen_US
dc.subjectunifyen_US
dc.titleMultidataset Independent Subspace Analysis with Application to Multimodal Fusionen_US
dc.typeTexten_US

Files

License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.56 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection