Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multilinear Analysis of Image Ensembles: TensorFaces
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Machine Learning
On the Euclidean Distance of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Introduction to 3D Computer Vision Techniques and Algorithms
An Introduction to 3D Computer Vision Techniques and Algorithms
Handwritten digit classification using higher order singular value decomposition
Pattern Recognition
Journal of Cognitive Neuroscience
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
Editorial: advances in nonnegative matrix and tensor factorization
Computational Intelligence and Neuroscience - Advances in Nonnegative Matrix and Tensor Factorization
Tensor Decompositions and Applications
SIAM Review
Computer Vision: Algorithms and Applications
Computer Vision: Algorithms and Applications
Ensemble of tensor classifiers based on the higher-order singular value decomposition
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
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The paper presents architecture and properties of the ensemble of the classifiers operating in the tensor orthogonal spaces obtained with the Higher-Order Singular Value Decomposition of prototype tensors. In this paper two modifications to this architecture are proposed. The first one consists in embedding of the Extended Euclidean Distance metric which accounts for the spatial relationship of pixels in the input images and allows robustness to small geometrical perturbations of the patterns. The second improvement consists in application of the weighted majority voting for combination of the responses of the classifiers in the ensemble. The experimental results show that the proposed improvements increase overall accuracy of the ensemble.