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In this paper we propose a novel dimensionality reduction method that is based on successive Laplacian SVM projections in orthogonal deflated subspaces. The proposed method, called Laplacian Support Vector Analysis, produces projection vectors, which capture the discriminant information that lies in the subspace orthogonal to the standard Laplacian SVMs. We show that the optimal vectors on these deflated...
In order to solve the problem of high dimension in text classification, this paper imported local linear embedding algorithm for dimension reduction. However, the original LLE did not necessarily make the loss of information minimize in process of reduction, so we combinated its two loss function together and improved it firstly. Then, linked the improved LLE and supervised learning and support vector...
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