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A new unsupervised discriminant projection for dimensionality reduction of high dimensional data is presented in this paper. The new method is a linear projection based on both the local and nonlocal statistically quantities. The discriminant criterion function be characterized by difference between the nonlocal scatter and the local scatter of feature vector, seeking to find a group of projection...
This paper presents a new method of dimensionality reduction of high dimensional data. The new discriminant criterion function be characterized by between the nonlocal scatter and the local scatter, and to directly construct between local scatter matrix and nonlocal scatter matrix by sample image matrixes. The criterion main purpose is to find a group of projection axis that simultaneously maximizes...
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...
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