Ensemble of HOSVD Generated Tensor Subspace Classifiers with Optimal Tensor Flattening Directions

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9648)


The paper presents a modified method of building ensembles of tensor classifiers for direct multidimensional pattern recognition in tensor subspaces. The novelty of the proposed solution is a method of lowering tensor subspace dimensions by rotation of the training pattern to their optimal directions. These are obtained computing and analyzing phase histograms of the structural tensor computed from the training images. The proposed improvement allows for a significant increase of the classification accuracy which favorably compares to the best methods cited in literature.


Pattern classification Ensemble of classifiers Tensor Higher-Order Singular Value Decomposition 



This work was supported by the Polish National Science Centre under the grant no. DEC-2013/09/B/ST6/02264. 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 ( All computer experiments were carried out using computer equipment sponsored by ENGINE project.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.AGH University of Science and TechnologyKrakówPoland
  2. 2.Wroclaw University of TechnologyWrocławPoland

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