On Robust Computation of Tensor Classifiers Based on the Higher-Order Singular Value Decomposition
In this paper a method of faster training of the ensembles of the tensor classifiers based on the Higher-Order Singular Value Decomposition is presented. The method relies on the fixed-point method of eigenvector computation which is employed at the stage of subspace construction of the flattened versions of the input tensor patterns. As verified experimentally, the proposed method allows up to five times speed-up factor at no significant difference in accuracy.
KeywordsTensor classifiers Subspace classification Higher-Order singular value decomposition
This work was supported by the Polish National Science Centre under the grant no. DEC-2014/15/B/ST6/00609. This work was supported by EC under FP7, Coordination and Support Action, Grant Agreement Number 316097, ENGINE—European Research Centre of Network Intelligence for Innovation Enhancement (http://engine.pwr.wroc.pl/). All computer experiments were carried out using computer equipment sponsored by ENGINE project.
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