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In supervised learning the parameters of a parametric Euclidean distance or mahalanobis distance can be effectively learned by so called Matrix Relevance Learning. This adaptation is not only useful to improve the discrimination capabilities of the model, but also to identify relevant features or relevant correlated features in the input data. Classical Matrix Relevance Learning scales quadratic with...
In this work, an H2 matrix was constructed to represent the dense system matrix resulting from integral-equation-based analysis of electrodynamic problems. The H- and H2-matrix framework is a general mathematical framework that enables a highly compact representation and efficient numerical computation of dense matrices. Here, fast multiple based methods, fast low-rank compression methods, and FFT-based...
With the recent progress in kernel based learning methods, computation with Gram matrices has received immense attention. However, the complexity of computing the entire Gram matrix is quadratic in terms of number of samples. Therefore, a considerable amount of work has been focused on extracting relevant information from the Gram matrix without accessing all the elements. Most of these methods exploits...
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