On optimal low rank Tucker approximation for tensors: the case for an adjustable core size

Bilian Chen (), Zhening Li () and Shuzhong Zhang ()

Journal of Global Optimization, 2015, vol. 62, issue 4, 832 pages

Abstract: Approximating high order tensors by low Tucker-rank tensors have applications in psychometrics, chemometrics, computer vision, biomedical informatics, among others. Traditionally, solution methods for finding a low Tucker-rank approximation presume that the size of the core tensor is specified in advance, which may not be a realistic assumption in many applications. In this paper we propose a new computational model where the configuration and the size of the core become a part of the decisions to be optimized. Our approach is based on the so-called maximum block improvement method for non-convex block optimization. Numerical tests on various real data sets from gene expression analysis and image compression are reported, which show promising performances of the proposed algorithms. Copyright Springer Science+Business Media New York 2015

Keywords: Multiway array; Tucker decomposition; Low-rank approximation; Maximum block improvement (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/s10898-014-0231-x

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