Name
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 l(0)-penalized least-square regression. While the least-squares regression ensures fitting fidelity, the l(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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ACCESS.2021.3128902 | IEEE ACCESS |
Keywords | DocType | Volume |
Piecewise polynomials, breakpoint detection, segmentation, curve fitting, sparse modeling | Journal | 9 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junbo Duan | 1 | 54 | 7.79 |
| Qing Wang | 2 | 345 | 76.64 |
| Yu-Ping Wang | 3 | 0 | 0.34 |