Piecewise Linear Regression Based On Adaptive Tree Structure Using Second Order Methods

We introduce a highly efficient online nonlinear regression algorithm. We process the data in a truly online manner such that no storage is needed, i.e., the data is discarded after used. For nonlinear modeling we use a hierarchical piecewise linear approach based on the notion of decision trees, where the regressor space is adaptively partitioned based directly on the performance. As the first time in the literature, we learn both the piecewise linear partitioning of the regressor space as well as the linear models in each region using highly effective second order methods, i.e., Newton-Raphson Methods. Hence, we avoid the well known over fitting issues and achieve substantial performance compared to the state of the art. We demonstrate our gains over the well known benchmark data sets and provide performance results in an individual sequence manner guaranteed to hold without any statistical assumptions.