Explicit segmentation is the partitioning of data into homogeneous regions by specifying cut-points. W. D. Fisher (1958) gave an early example of explicit segmentation based on the minimisation of squared error. Fisher called this the grouping problem and came up with a polynomial time Dynamic Programming Algorithm (DPA). Oliver, Baxter and colleagues (1996,1997,1998) have applied the information-theoretic Minimum Message Length (MML) principle to explicit segmentation. They have derived formulas for specifying cut-points imprecisely and have empirically shown their criterion to be superior to other segmentation methods (AIC, MDL and BIC). We use a simple MML criterion and Fisher’s DPA to perform numerical Bayesian (summing and) integration (using message lengths) over the cut-point location parameters. This gives an estimate of the number of segments, which we then use to estimate the cut-point positions and segment parameters by minimising the MML criterion. This is shown to have lower Kullback-Leibler distances on generated data.
CITATION STYLE
Fitzgibbon, L. J., Allison, L., & Dowe, D. L. (2000). Minimum message length grouping of ordered data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1968, pp. 56–70). Springer Verlag. https://doi.org/10.1007/3-540-40992-0_5
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