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 language | English (US) |
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Title of host publication | Proceedings of the IEEE International Conference on Computer Vision |
Pages | 1348-1355 |
Number of pages | 8 |
DOIs | |
State | Published - 2011 |
Event | 2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011 - Barcelona, Spain Duration: Nov 6 2011 → Nov 13 2011 |
Other
Other | 2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011 |
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Country/Territory | Spain |
City | Barcelona |
Period | 11/6/11 → 11/13/11 |
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition