Abstract
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.
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Acknowledgments
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 (http://engine.pwr.wroc.pl/). All computer experiments were carried out using computer equipment sponsored by ENGINE project.
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Cyganek, B., Woźniak, M. (2016). Efficient Multidimensional Pattern Recognition in Kernel Tensor Subspaces. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2016. Lecture Notes in Computer Science(), vol 9714. Springer, Cham. https://doi.org/10.1007/978-3-319-40973-3_54
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DOI: https://doi.org/10.1007/978-3-319-40973-3_54
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