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Evrim Acar, Daniel M. Dunlavy and Tamara G. Kolda A scalable optimization approach for fitting canonical tensor decompositions Journal of Chemometrics 25

Version of Record online: 27 JAN 2011 | DOI: 10.1002/cem.1335

Tensor decompositions are higher-order analogues of matrix decompositions and have proven to be powerful tools for data analysis. For fitting the CANDECOMP/PARAFAC tensor decomposition, we propose the use of gradient-based optimization methods such as nonlinear conjugate gradients. Computational experiments demonstrate that the gradient-based optimization methods are more accurate than the standard alternating least-squares (ALS) and faster than second-order optimizaion in terms of total computation time.

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