Distance Rank Score: Unsupervised filter method for feature selection on
  imbalanced dataset
Katarina Firdova Céline Labart Arthur Martel
This paper presents a new filter method for unsupervised feature selection.
This method is particularly effective on imbalanced multi-class dataset, as in
case of clusters of different anomaly types. Existing methods usually involve
the variance of the features, which is not suitable when the different types of
observations are not represented equally. Our method, based on Spearman's Rank
Correlation between distances on the observations and on feature values, avoids
this drawback. The performance of the method is measured on several clustering
problems and is compared with existing filter methods suitable for unsupervised
data.