The generic problem in anomaly detection is identifying unusual samples present in a large population. Each member of the population is described by a list of characteristics that define a feature vector. One statistical method that accounts for mutual correlations among the components has defined the standard for anomaly detection in communication, radar, and hyperspectral signal processing for several decades. This paper describes an advanced methodology that constructs nonlinear transformations to account for observed data distributions not amenable to a statistical description. The construction relies on a combination of stochastic methods and phenomenological constraints. Examples are taken from hyperspectral target detection.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Advanced Methods of Multivariate Anomaly Detection


    Contributors:
    Schaum, A. (author)


    Publication date :

    2007-03-01


    Size :

    401697 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Multivariate Hierarchical Anomaly Detection

    UCAR SEYHAN / MERCER RYAN | European Patent Office | 2022

    Free access

    MIM-GAN-based Anomaly Detection for Multivariate Time Series Data

    Lu, Shan / Dong, Zhicheng / Cai, Donghong et al. | IEEE | 2023


    ANOMALY DETECTION SYSTEM, ANOMALY DETECTION APPARATUS, AND ANOMALY DETECTION METHOD

    KOBAYASHI AYUMI | European Patent Office | 2023

    Free access

    ANOMALY DETECTION SYSTEMS AND METHODS

    PESE MERT DIETER / JOSHI PRACHI / TEPE KEMAL E | European Patent Office | 2022

    Free access

    Anomaly detection systems and methods

    PESE MERT DIETER / JOSHI PRACHI / TEPE KEMAL E | European Patent Office | 2023

    Free access