Inexact Proximal Conjugate Subgradient Algorithm for fMRI Data Completion

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dc.contributor.authorBelyaeva, Irina
dc.contributor.authorLong, Qunfang
dc.contributor.authorAdali, Tulay
dc.date.accessioned2020-11-19T19:35:47Z
dc.date.available2020-11-19T19:35:47Z
dc.date.issued2020
dc.descriptionEUSIPCO 2020en_US
dc.description.abstractTensor representations have proven useful for many problems, including data completion. A promising application for tensor completion is functional magnetic resonance imaging (fMRI) data that has an inherent four-dimensional (4D) structure and is prone to missing voxels and regions due to issues in acquisition. A key component of successful tensor completion is a rank estimation. While widely used as a convex relaxation of the tensor rank, tensor nuclear norm (TNN) imposes strong low-rank constraints on all tensor modes to be simultaneously low-rank and often leads to suboptimal solutions. We propose a novel tensor completion model in tensor train (TT) format with a proximal conjugate subgradient (PCS-TT) method for solving the nonconvex rank minimization problem by using properties of Moreau’s decomposition. PCS-TT allows the use of a wide range of robust estimators and can be used for data completion and sparse signal recovery problems. We present experimental results for data completion in fMRI, where PCS-TT demonstrates significant improvements compared with competing methods. In addition, we present results that demonstrate the advantages of considering the 4D structure of the fMRI data. as opposed to using three- and two-dimensional representations that have dominated the work on fMRI analysis.en_US
dc.description.urihttps://www.eurasip.org/Proceedings/Eusipco/Eusipco2020/pdfs/0001025.pdfen_US
dc.format.extent5 pagesen_US
dc.genreconference papers and proccedingsen_US
dc.identifierdoi:10.13016/m2qx4h-goq2
dc.identifier.citationBelyaeva, Irina; Long, Qunfang; Adali, Tulay; Inexact Proximal Conjugate Subgradient Algorithm for fMRI Data Completion; EUSIPCO 2020; https://www.eurasip.org/Proceedings/Eusipco/Eusipco2020/pdfs/0001025.pdfen_US
dc.identifier.urihttp://hdl.handle.net/11603/20110
dc.language.isoen_USen_US
dc.publisherEuropean Association for Signal Processing (EURASIP)en_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Student 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.subjecttensor representationsen_US
dc.subjectfunctional magnetic resonance imaging (fMRI)en_US
dc.subjecttensor nuclear norm (TNN)en_US
dc.titleInexact Proximal Conjugate Subgradient Algorithm for fMRI Data Completionen_US
dc.typeTexten_US

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