A unified framework for supervised learning of semantic models

Yicheng Wen, Soumalya Sarkar, Asok Ray, Xin Jin, Thyagaraju Damarla

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

    Abstract

    Patterns of interest in dynamical systems are often represented by a number of semantic features such as probabilistic finite state automata (PFSA) and cross machines over possibly different alphabets. Previous publications have reported a Hilbert space formulation of PFSA over the same alphabet. This paper introduces an isomorphism between the Hilbert space of PFSA and the Euclidean space to improve the computational efficiency of algebraic operations. Furthermore, this formulation is extended to cross machines and it shows that these semantic features can be structured in a unified mathematical framework. In this framework, an algorithm of supervised learning is formulated for generating semantic features in the setting of linear discriminant analysis (LDA). The proposed algorithm has the flexibility for adaptation under different environments by tuning a set of parameters that can be updated autonomously or be specified by the human user. The proposed algorithm has been validated on real-life data for target detection as applied to border control.

    Original languageEnglish (US)
    Title of host publication2012 American Control Conference, ACC 2012
    Pages2183-2188
    Number of pages6
    StatePublished - 2012
    Event2012 American Control Conference, ACC 2012 - Montreal, QC, Canada
    Duration: Jun 27 2012Jun 29 2012

    Other

    Other2012 American Control Conference, ACC 2012
    CountryCanada
    CityMontreal, QC
    Period6/27/126/29/12

    All Science Journal Classification (ASJC) codes

    • Electrical and Electronic Engineering

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  • Cite this

    Wen, Y., Sarkar, S., Ray, A., Jin, X., & Damarla, T. (2012). A unified framework for supervised learning of semantic models. In 2012 American Control Conference, ACC 2012 (pp. 2183-2188). [6314719]