The NEU Meta-Algorithm for Geometric Learning with Applications in Finance
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We introduce a meta-algorithm, called non-Euclidean upgrading (NEU), which learns algorithm-specific geometries to improve the training and validation set performance of a wide class of learning algorithms. Our approach is based on iteratively performing local reconfigurations of the space in which the data lie. These reconfigurations build universal approximation and universal reconfiguration properties into the new algorithm being learned. This allows any set of features to be learned by the new algorithm to arbitrary precision. The training and validation set performance of NEU is investigated through implementations predicting the relationship between select stock prices as well as finding low-dimensional representations of the German Bond yield curve.
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