Title
Linear space direct pattern sampling using coupling from the past Linear space direct pattern sampling using coupling from the past
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Publication type
bookPart
Publication
New York, N.Y. :ACM, [*]
Subject
Computer. Automation
Source (book)
KDD '12 Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
ISBN - Hoofdstuk
978-1-4503-1462-6
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
This paper shows how coupling from the past (CFTP) can be used to avoid time and memory bottlenecks in direct local pattern sampling procedures. Such procedures draw controlled amounts of suitably biased samples directly from the pattern space of a given dataset in polynomial time. Previous direct pattern sampling methods can produce patterns in rapid succession after some initial preprocessing phase. This preprocessing phase, however, turns out to be prohibitive in terms of time and memory for many datasets. We show how CFTP can be used to avoid any super-linear preprocessing and memory requirements. This allows to simulate more complex distributions, which previously were intractable. We show for a large number of public real-world datasets that these new algorithms are fast to execute and their pattern collections outperform previous approaches both in unsupervised as well as supervised contexts.
Handle