HOPS: A Fast Algorithm for Segmenting Piecewise Polynomials of Arbitrary Orders

Abstract

The segmentation of piecewise polynomial signals arises in a variety of scientific and engineering fields. When a signal is modeled as a piecewise polynomial, the key then becomes the detection of breakpoints followed by curve fitting and parameter estimation. This paper proposes HOPS, a fast High-Order Polynomial Segmenter, which is based on 0 -penalized least-square regression. While the least-squares regression ensures fitting fidelity, the 0 penalty takes the number of breakpoints into account. We show that dynamic programming can be applied to find the optimal solution to this problem and that a pruning strategy and matrix factorization can be utilized to accelerate the execution speed. Finally, we provide some illustrative examples, and compare the proposed method with state-of-the-art alternatives.

Automatic Piecewise Extreme Learning Machine-Based Model for S-Parameters of RF Power Amplifier

“…For example, it can be achieved based on the parameter extraction process in actual experiments [17,18] or according to the curve trend of the parameters [7,19]. Otherwise, dynamic programming is also commonly applied to find the optimal solution for segmentation [20]. This paper proposes a segmentation strategy based on the changing points of concave-convex parameter characteristics.…”