Unsupervised inductive learning in symbolic sequences via recursive identification of self-similar semantics

Ishanu Chattopadhyay, Yicheng Wen, Asok Ray, Shashi Phoha

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

This paper presents a new pattern discovery algorithm for constructing probabilistic finite state automata (PFSA) from symbolic sequences. The new algorithm, described as Compression via Recursive Identification of Self-Similar Semantics (CRISSiS), makes use of synchronizing strings for PFSA to localize particular states and then recursively identifies the rest of the states by computing the n-step derived frequencies. We compare our algorithm to other existing algorithms, such as D-Markov and Casual-State Splitting Reconstruction (CSSR) and show both theoretically and experimentally that our algorithm captures a larger class of models.

Original languageEnglish (US)
Title of host publicationProceedings of the 2011 American Control Conference, ACC 2011
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages125-130
Number of pages6
ISBN (Print)9781457700804
DOIs
StatePublished - Jan 1 2011

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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    Chattopadhyay, I., Wen, Y., Ray, A., & Phoha, S. (2011). Unsupervised inductive learning in symbolic sequences via recursive identification of self-similar semantics. In Proceedings of the 2011 American Control Conference, ACC 2011 (pp. 125-130). [5991453] (Proceedings of the American Control Conference). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/acc.2011.5991453