Joint SVD-Hyperalignment for multi-subject FMRI data alignment

Po Hsuan Chen, J. Swaroop Guntupalli, James V. Haxby, Peter J. Ramadge

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Inter-subject alignment is an important aspect of multi-subject fMRI research. Recently a method known as Hyperalignment has shown considerable success in attaining such alignment. In order to improve computational efficiency, we investigate a joint SVD-Hyperalignment algorithm. We show that this algorithm is more scalable than the standard Hyperalignment algorithm by providing analytic and empirical results using a multi-subject fMRI dataset. The experimental results show improved computation speed while maintaining between subject prediction accuracy on an image viewing experiment. In addition, our results provide benchmark relationships between voxel selection, accuracy and computation complexity for Hyperalignment, taking a joint SVD of the data, and joint SVD-Hyperalignment.

Original languageEnglish (US)
Title of host publicationIEEE International Workshop on Machine Learning for Signal Processing, MLSP
EditorsTulay Adali, Jan Larsen, Mamadou Mboup, Eric Moreau
PublisherIEEE Computer Society
ISBN (Electronic)9781479936946
DOIs
StatePublished - Nov 14 2014
Event2014 24th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2014 - Reims, France
Duration: Sep 21 2014Sep 24 2014

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Other

Other2014 24th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2014
CountryFrance
CityReims
Period9/21/149/24/14

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Signal Processing

Keywords

  • Alignment
  • Dimensionality Reduction
  • Procrustes Problems
  • fMRI

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  • Cite this

    Chen, P. H., Guntupalli, J. S., Haxby, J. V., & Ramadge, P. J. (2014). Joint SVD-Hyperalignment for multi-subject FMRI data alignment. In T. Adali, J. Larsen, M. Mboup, & E. Moreau (Eds.), IEEE International Workshop on Machine Learning for Signal Processing, MLSP [6958912] (IEEE International Workshop on Machine Learning for Signal Processing, MLSP). IEEE Computer Society. https://doi.org/10.1109/MLSP.2014.6958912