Efficient Multidimensional Pattern Recognition in Kernel Tensor Subspaces

  • Bogusław CyganekEmail author
  • Michał Woźniak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9714)


In this paper we discuss algorithmically efficient methods of multidimensional patter recognition in kernel tensor subspaces. The kernel principal component analysis, which originally operates only on vector data, is joined with the tensor chordal kernel which opens a way of direct usage of the multidimensional signals, such as color video streams, seismic signals or hyperspectral images. We address the problem of efficient implementation of the eigendecomposition problem which is a core algorithm for both methods. For this the fixed point algorithm is employed. We show usefulness of this approach on the problem of visual pattern recognition and show speed-up ratio when using the proposed implementation.


Kernel PCA Chordal kernel Tensor Subspace classification 



This work was supported by the Polish National Science Center under the grant No. NCN DEC-2014/15/B/ST6/00609.

This work was also 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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