Title
Fast sequence segmentation using log-linear models Fast sequence segmentation using log-linear models
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Publication type
article
Publication
Boston, Mass. ,
Subject
Computer. Automation
Source (journal)
Data mining and knowledge discovery. - Boston, Mass.
Volume/pages
27(2013) :3 , p. 421-441
ISSN
1384-5810
ISI
000322454200007
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Sequence segmentation is a well-studied problem, where given a sequence of elements, an integer K, and some measure of homogeneity, the task is to split the sequence into K contiguous segments that are maximally homogeneous. A classic approach to find the optimal solution is by using a dynamic program. Unfortunately, the execution time of this program is quadratic with respect to the length of the input sequence. This makes the algorithm slow for a sequence of non-trivial length. In this paper we study segmentations whose measure of goodness is based on log-linear models, a rich family that contains many of the standard distributions. We present a theoretical result allowing us to prune many suboptimal segmentations. Using this result, we modify the standard dynamic program for 1D log-linear models, and by doing so reduce the computational time. We demonstrate empirically, that this approach can significantly reduce the computational burden of finding the optimal segmentation.
E-info
https://repository.uantwerpen.be/docman/iruaauth/08dfbd/42f5326.pdf
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Handle