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It is a great challenge for face recognition with single training sample per person. In this paper, we try to propose a new algorithm based sparse representation to solve this problem. The algorithm takes the two-dimensional training samples as the training set directly rather than image vectors. So we can obtain the dictionary of sparse representation only using one sample. The proposed algorithm...
Two-Dimension Linear Discriminant Analysis (2DLDA) and complex-matrix LDA are two noticeable improvements to conventional LDA. They can achieve a good performance respectively. However, the complex-matrix LDA is very suitable for bimodal biometrics. In this paper, we indicate the two available implementation procedures of complex-matrix, i.e. the original implementation procedure and one alternative...
The identification process of the classical Preisach model which is based on a neural network approach is presented. The fundamental idea of this approach is to identify Preisach function by training a neural network with a set of loops whose identification function is already known. The suggested identification approach has been numerically implemented and carried out for a fast tool servo system...
This study proposes an extended subspace method (ESM) in feature extraction and dimension-reduction problems for land cover classification of hyperspectral and multi-spectral remote sensing images. The main idea of our method is to use a multiple similarity method (MSM) onto an averaged learning subspace method (ALSM) and makes use of fidelity value criteria in the selection of the optimal subspace...
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