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

Masato Ohkubo, Yasushi Nagata

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

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)1213-1227
Number of pages15
JournalTotal Quality Management and Business Excellence
Volume29
Issue number9-10
DOIs
Publication statusPublished - 2018 Jul 29

Keywords

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

ASJC Scopus subject areas

  • Business, Management and Accounting(all)

Fingerprint Dive into the research topics of 'Anomaly detection in high-dimensional data with the Mahalanobis–Taguchi system'. Together they form a unique fingerprint.

Cite this