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Unsupervised discriminant projection (UDP) has a good effect on face recognition problem, but it has not made full use of the training samples' class information that is useful for classification. Linear discrimination analysis (LDA) is a classical face recognition method. It is effective for classification, but it can not discover the samples' nonlinear structure. This paper develops a manifold-based...
This paper introduces the minimal local reconstruction error (MLRE) as a similarity measure and presents a MLRE-based classier. From the geometric meaning of the minimal local reconstruction error, we derive that the MLRE-based classifier is a generalization of the conventional nearest neighbor classier and the nearest neighbor line and plane classifiers. We further apply the MLRE measure to characterize...
A novel model for image feature extraction and recognition called enhanced two-dimension scatter difference discriminant analysis (E2DSDD) is presented in the paper. 2DSDD can extract less coefficients than the traditional two-dimension scatter difference discriminant analysis (2DSDD) for image representation and lead to faster classification. In addition, a new feature selection scheme is suggested...
This paper develops an unsupervised discriminant projection (UDP) technique for dimensionality reduction of high-dimensional data in small sample size cases. UDP can be seen as a linear approximation of a multimanifolds-based learning framework which takes into account both the local and nonlocal quantities. UDP characterizes the local scatter as well as the nonlocal scatter, seeking to find a projection...
As nonlinear feature extraction methods, kernel methods have been widely applied in pattern recognition. However, for high dimensional data such as face images, a kernel method corresponds to a high computational cost. In this paper, a novel idea and framework are presented to implement the kernel methods on high-dimensional data. A remarkable character of the framework is that there are two feature...
In this paper, a real face image is regarded as the result of adding the so-called "standard" face image under an ideal illumination condition to the corresponding "error image", which reflects the imaging difference between the real illumination and the ideal illumination. Furthermore, based on two propositions, we infer that for two images of the same face the correlation between...
This paper develops an unsupervised discriminant projection (UDP) technique for feature extraction. UDP takes the local and non-local information into account, seeking to find a projection that maximizes the non-local scatter and minimizes the local scatter simultaneously. This characteristic makes UDP more intuitive and more powerful than the up-to-date method - locality preserving projection (LPP,...
Linear discriminant analysis (LDA) is one of the most popular methods in feature extraction and dimension reduction. However, in many real applications, particularly in image recognition applications such as face recognition, conventional LDA algorithm will often encounter small sample size problem. In this paper, an effective classification image space is defined and optimal features are extracted...
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