Vector space formulation of probabilistic finite state automata

Yicheng Wen, Asok Ray

    Research output: Contribution to journalArticle

    4 Citations (Scopus)

    Abstract

    This paper develops a vector space model of a class of probabilistic finite state automata (PFSA) that are constructed from finite-length symbol sequences. The vector space is constructed over the real field, where the algebraic operations of vector addition and the associated scalar multiplication operations are defined on a probability measure space, and implications of these algebraic operations are interpreted. The zero element of this vector space is semantically equivalent to a PFSA, referred to as symbolic white noise. A norm is introduced on the vector space of PFSA, which provides a measure of the information content. An application example is presented in the framework of pattern recognition for identification of robot motion in a laboratory environment.

    Original languageEnglish (US)
    Pages (from-to)1127-1141
    Number of pages15
    JournalJournal of Computer and System Sciences
    Volume78
    Issue number4
    DOIs
    StatePublished - Jul 1 2012

    Fingerprint

    Finite State Automata
    Finite automata
    Vector spaces
    Vector space
    Formulation
    Vector addition
    Additive identity
    Vector Space Model
    Scalar multiplication
    Measure space
    Information Content
    Probability Space
    White noise
    Probability Measure
    Pattern Recognition
    Robot
    Pattern recognition
    Norm
    Motion
    Robots

    All Science Journal Classification (ASJC) codes

    • Theoretical Computer Science
    • Computer Networks and Communications
    • Computational Theory and Mathematics
    • Applied Mathematics

    Cite this

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    Vector space formulation of probabilistic finite state automata. / Wen, Yicheng; Ray, Asok.

    In: Journal of Computer and System Sciences, Vol. 78, No. 4, 01.07.2012, p. 1127-1141.

    Research output: Contribution to journalArticle

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