Efficient robust methods via monitoring for clustering and multivariate data analysis
作者：
Highlights：
• A new data-dependent method for determining the trimming level in robust clustering.
• We monitor the cluster structure as the trimming level changes.
• The robust clustering constrains the ratio of eigenvalues of dispersion matrices.
• The “car-bike” plot exhibits cluster stability as the eigenvalue constraint varies.
• Sizable bibliography relating the work to that in the machine learning and pattern recognition fields.
摘要
•A new data-dependent method for determining the trimming level in robust clustering.•We monitor the cluster structure as the trimming level changes.•The robust clustering constrains the ratio of eigenvalues of dispersion matrices.•The “car-bike” plot exhibits cluster stability as the eigenvalue constraint varies.•Sizable bibliography relating the work to that in the machine learning and pattern recognition fields.
论文关键词：Bovine phlegmon,“Car-bike” plot,Clustering,Eigenvalue constraint,Forward search,MCD,MM-Estimation,Modified BIC,Outliers
论文评审过程：Received 18 January 2018, Revised 17 October 2018, Accepted 17 November 2018, Available online 19 November 2018, Version of Record 26 November 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.11.016
