Embedding of the Extended Euclidean Distance into Pattern Recognition with Higher-Order Singular Value Decomposition of Prototype Tensors - Archive ouverte HAL Access content directly
Conference Papers Year : 2012

Embedding of the Extended Euclidean Distance into Pattern Recognition with Higher-Order Singular Value Decomposition of Prototype Tensors

(1)
1

Abstract

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.
Fichier principal
Vignette du fichier
978-3-642-33260-9_15_Chapter.pdf (328.03 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01551742 , version 1 (30-06-2017)

Licence

Attribution - CC BY 4.0

Identifiers

Cite

Bogusław Cyganek. Embedding of the Extended Euclidean Distance into Pattern Recognition with Higher-Order Singular Value Decomposition of Prototype Tensors. 11th International Conference on Computer Information Systems and Industrial Management (CISIM), Sep 2012, Venice, Italy. pp.180-190, ⟨10.1007/978-3-642-33260-9_15⟩. ⟨hal-01551742⟩
34 View
68 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More