Universal Nonlinear Regression on High Dimensional Data Using Adaptive Hierarchical Trees

We study online sequential regression with nonlinearity and time varying statistical distribution when the regressors lie in a high dimensional space. We escape the curse of dimensionality by tracking the subspace of the underlying manifold using a hierarchical tree structure. We use the projections of the original high dimensional regressor space onto the underlying manifold as the modified regressor vectors for modeling of the nonlinear system. By using the proposed algorithm, we reduce the computational complexity to the order of the depth of the tree and the memory requirement to only linear in the intrinsic dimension of the manifold. The proposed techniques are specifically applicable to high dimensional streaming data analysis in a time varying environment. We demonstrate the significant performance gains in terms of mean square error over the other state of the art techniques through simulated as well as real data.