A multi-affine model for tensor decomposition

Yiqing Yang, Li Zhang, Sen Wang, Hongrui Jiang, Christopher J Murphy, Jim Ver Hoeve

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

1 Scopus citations

Abstract

Higher-order Singular Value Decomposition (HOSVD) for tensor decomposition is widely used in multi-variate data analysis, and has shown applications in several areas in computer vision in the last decade. Conventional multi-linear assumption in HOSVD is not translation invariant translation in different tensor modes can yield different decomposition results. The translation is difficult to remove as preprocessing when the tensor data has missing data entries. In this paper we propose a more general multi-affine model by adding appropriate constant terms in the multi-linear model. The multi-affine model can be computed by generalizing the HOSVD algorithm; the model performs better for filling in missing values in data tensor during model training, as well as for reconstructing missing values in new mode vectors during model testing, on both synthetic and real data.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE International Conference on Computer Vision
Pages1348-1355
Number of pages8
DOIs
StatePublished - 2011
Event2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011 - Barcelona, Spain
Duration: Nov 6 2011Nov 13 2011

Other

Other2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011
CountrySpain
CityBarcelona
Period11/6/1111/13/11

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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    Yang, Y., Zhang, L., Wang, S., Jiang, H., Murphy, C. J., & Ver Hoeve, J. (2011). A multi-affine model for tensor decomposition. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1348-1355). [6130408] https://doi.org/10.1109/ICCVW.2011.6130408