An algorithm for optimal partitioning of data on an interval

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/sup 2/). The al...

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Bibliographic Details
Published inIEEE signal processing letters Vol. 12; no. 2; pp. 105 - 108
Main Authors Jackson, B., Scargle, J.D., Barnes, D., Arabhi, S., Alt, A., Gioumousis, P., Gwin, E., Sangtrakulcharoen, P., Tan, L., Tun Tao Tsai
Format Journal Article
LanguageEnglish
Published IEEE 01.02.2005
Subjects
Algorithm design and analysis
Bayesian methods
Bayesian modeling
cluster analysis
Clustering algorithms
density estimation
histograms
Iterative algorithms
Mathematics
NASA
optimization
Partitioning algorithms
Signal analysis
Signal detection
Signal processing algorithms
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Summary: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/sup 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.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2001.838216