An Algorithm for Optimal Partitioning of Data on an Interval
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arXiv
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| Hauptverfasser: | , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2003
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| _version_ | 1866917022775902208 |
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| author | Jackson, Brad Scargle, Jeffrey D. Barnes, David Arabhi, Sundararajan Alt, Alina Gioumousis, Peter Gwin, Elyus Sangtrakulcharoen, Paungkaew Tan, Linda Tsai, Tun Tao |
| author_facet | Jackson, Brad Scargle, Jeffrey D. Barnes, David Arabhi, Sundararajan Alt, Alina Gioumousis, Peter Gwin, Elyus Sangtrakulcharoen, Paungkaew Tan, Linda Tsai, Tun Tao |
| contents | Many signal processing problems can be solved by maximizing the fitness of a segmented model over all possible partitions of the data interval. This letter describes a simple but powerful algorithm that searches the exponentially large space of partitions of $N$ data points in time $O(N^2)$. The algorithm is guaranteed to find the exact global optimum, automatically determines the model order (the number of segments), has a convenient real-time mode, can be extended to higher dimensional data spaces, and solves a surprising variety of problems in signal detection and characterization, density estimation, cluster analysis and classification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_math_0309285 |
| institution | arXiv |
| publishDate | 2003 |
| record_format | arxiv |
| spellingShingle | An Algorithm for Optimal Partitioning of Data on an Interval Jackson, Brad Scargle, Jeffrey D. Barnes, David Arabhi, Sundararajan Alt, Alina Gioumousis, Peter Gwin, Elyus Sangtrakulcharoen, Paungkaew Tan, Linda Tsai, Tun Tao Numerical Analysis Astrophysics Computational Engineering, Finance, and Science Data Structures and Algorithms Information Theory Combinatorics 65C60 Many signal processing problems can be solved by maximizing the fitness of a segmented model over all possible partitions of the data interval. This letter describes a simple but powerful algorithm that searches the exponentially large space of partitions of $N$ data points in time $O(N^2)$. The algorithm is guaranteed to find the exact global optimum, automatically determines the model order (the number of segments), has a convenient real-time mode, can be extended to higher dimensional data spaces, and solves a surprising variety of problems in signal detection and characterization, density estimation, cluster analysis and classification. |
| title | An Algorithm for Optimal Partitioning of Data on an Interval |
| topic | Numerical Analysis Astrophysics Computational Engineering, Finance, and Science Data Structures and Algorithms Information Theory Combinatorics 65C60 |
| url | https://arxiv.org/abs/math/0309285 |