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For tensor decompositions such as HOSVD and ParaFac, the objective functions are nonconvex.
Analysis of individual differences in multidimensional scaling via an n-way generalization of Eckart-Young decomposition
J. Carroll and J. Chang · 1970
Earlier work this paper cites.
Foundations of the parafac procedure: Model and conditions for an ’explanatory’ multi-mode factor analysis
R. Harshman · 1970
Earlier work this paper cites.
A multilinear singular value decomposition
L. D. Lathauwer, B. D. Moor, and J. Vandewalle · 2000
Earlier work this paper cites.
Orthogonal tensor decompositions
T. Kolda · 2001
Earlier work this paper cites.
SIMPLIcity: Semantics-sensitive integrated matching for picture LIbraries
J. Z. Wang, J. Li, and G. Wiederhold · 2001
Cited alongside, same era.
Rank-one approximation to high order tensors
T. Zhang and G. H. Golub · 2001
Cited alongside, same era.
Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
P. Perona, R. Fergus, and F. F. Li · 2004
Cited alongside, same era.
Equivalence of non-iterative algorithms for simultaneous low rank approximations of matrices
K. Inoue and K. Urahama · 2006
Later among the works it cites.
Matlab tensor toolbox version 2.2
B. Bader and T. Kolda · 2007
Later among the works it cites.
Tensor reduction error analysis – applications to video compression and classification
C. Ding, H. Huang, and D. Luo · 2008
Later among the works it cites.
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