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We address nonlinear sequential regression in high dimensional settings when the data lies on a time varying manifold. We solve the curse of dimensionality by tracking the subspace of the underlying manifold using a hierarchical tree structure. Therefore, instead of using the original feature vectors as the input, we use the projections of the high dimensional feature space onto the underlying manifold...
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