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Linear Discriminant Analysis (LDA) has been widely used in appearance-based face recognition. However, it requires lots of training samples for each person with respect to the large dimensionality of the image space, which is difficult to collect in reality. To overcome the severe constraint of training sample deficiency, approaches based on single training sample per person (SSPP) arise in the past...
In this paper, a kernelized version of nonparametric discriminant analysis is proposed that we name KNDA. The main idea is to first map the original data into another high-dimensional space, and then to perform nonparametric discriminant analysis in the high dimensional space. Nonparametric discriminant analysis can relax the Gaussian assumption required for the classical linear discriminant analysis,...
Kernel method is a nonlinear feature extraction approach. Firstly, the samples in the original feature space are transformed into a higher dimensional feature space by nonlinear mapping. Then, linear approaches are used in the higher dimensional feature space, and thus nonlinear features of original samples are extracted. The Heteroscedastic Discriminant Analysis (HDA), in which the equal within-class...
The Kernel Support Vector Machine (KSVM) is a powerful nonlinear classification methodology where, the Support Vectors (SVs) fully describe the decision surface by incorporating local information in the Kernel space. On the other hand, the Kernel Fisher Discriminant(KFD) is a non-linear classifier which has proven to be powerful and competitive to several state-of-the-art classifiers. This paper proposes...
We propose signature linear discriminant analysis (signature-LDA) as an extension of LDA that can be applied to signatures, which are known to be more informative representations of local image features than vector representations, such as visual word histograms. Based on earth mover's distances between signatures, signature-LDA does not require vectorization of local image features in contrast to...
Kernel method is a nonlinear feature extraction approach. Firstly, the samples in the original feature space are transformed into a higher dimensional feature space by nonlinear mapping. Then, linear approaches are used in the higher dimensional feature space, and thus nonlinear features of original samples are extracted. The Heteroscedastic Discriminant Analysis (HDA), in which the equal within-class...
In this paper, we focus on face recognition over image sets, where each set is represented by a linear subspace. Linear Discriminant Analysis (LDA) is adopted for discriminative learning. After investigating the relation between regularization on Fisher Criterion and Maximum Margin Criterion, we present a unified framework for regularized LDA. With the framework, the ratio-form maximization of regularized...
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