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This paper makes two contributions to the problem of correcting non-Euclidean dissimilarities. Data of this sort arrise when there are negative Eigen values of the dissimilarity matrix, and can therefore not be embedded into a real-valued Euclidean space. Our first contribution is to show how the non-Euclidean artifacts can be rectified. This is achieved by applying Ricci flow to the embedding of...
For complex nonlinear systems of chemical industry process, traditional kernel principal component analysis (KPCA) methods are very difficult to calculate the kernel matrix for fault detection with large sample sets. So an improved fault detection method based on feature vector selection-KPCA (FVS-KPCA) is developed. This method can evidently reduce calculational complexity of fault detection and...
A linear blind source separation algorithm based on generalized eigen-equation resolving is presented. Then a nonlinear blind source separation algorithm is proposed by extending the linear source separation algorithm to the nonlinear domain. The received mixing signals are first mapped to high-dimensional kernel feature space, and a feature vector basis given by the fitness function of the kernel...
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