Ensemble of HOSVD Generated Tensor Subspace Classifiers with Optimal Tensor Flattening Directions
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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.
KeywordsPattern 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 (http://engine.pwr.wroc.pl/). All computer experiments were carried out using computer equipment sponsored by ENGINE project.
- 4.Cyganek, B.: Embedding of the extended euclidean distance into pattern recognition with higher-order singular value decomposition of prototype tensors. In: Cortesi, A., Chaki, N., Saeed, K., Wierzchoń, S. (eds.) CISIM 2012. LNCS, vol. 7564, pp. 180–190. Springer, Heidelberg (2012)CrossRefGoogle Scholar
- 5.Cyganek, B., Object Detection and Recognition in Digital Images: Theory and Practice, Wiley (2013)Google Scholar
- 15.Lathauwer, de L.: Signal processing based on multilinear algebra. Ph.D dissertation, Katholieke Universiteit Leuven (1997)Google Scholar
- 17.LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE Speech Image Process. 86(11), 2278–2324 (1998)Google Scholar
- 19.Maji, S., Malik, J.: Fast and Accurate Digit Classification. Technical report no. UCB/EECS-2009-159, University of California at Berkeley (2009)Google Scholar