Anomaly detection in high-dimensional data with the Mahalanobis–Taguchi system

Masato Ohkubo, Yasushi Nagata

    Research output: Contribution to journalArticle

    Abstract

    The Mahalanobis–Taguchi (MT) system is a typical Taguchi method and plays an important role in several fields. This study aims at improving the statistical procedure employed for anomaly detection in high-dimensional data with the MT system. The proposed study focuses on estimating the eigenvalues and eigenvectors of the covariance matrix and introduces an estimation procedure based on sparse principal component analysis (SPCA) in the MT system. By incorporating SPCA, eigenvalues and eigenvectors can be accurately estimated for high-dimensional data. In addition, the interpretation of the principal components can become simplified with decreasing number of nonzero elements in the estimated eigenvectors. Numerical experiments have confirmed that the proposed procedure is beneficial for both anomaly detection performance and investigating the cause of anomalies in high-dimensional data. Furthermore, a limitation of the proposed study is its emphasis on improving anomaly detection procedures founded on the first principal component and its residual component. However, the scope of such an anomaly detection procedure can be easily expanded for further improvement.

    Original languageEnglish
    Pages (from-to)1-15
    Number of pages15
    JournalTotal Quality Management and Business Excellence
    DOIs
    Publication statusAccepted/In press - 2018 Jun 15

    Fingerprint

    Anomaly detection
    Principal component analysis
    Eigenvalues
    Principal components
    Taguchi method
    Numerical experiment
    Covariance matrix
    Anomaly

    Keywords

    • Mahalanobis distance
    • Mahalanobis–Taguchi system
    • sparse principal component analysis
    • Taguchi method

    ASJC Scopus subject areas

    • Business, Management and Accounting(all)

    Cite this

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