  The paper deals with the adaptation of a new measure for the unsupervised
feature selection problems. The proposed measure is based on space filling
concept and is called the coverage measure. This measure was used for judging
the quality of an experimental space filling design. In the present work, the
coverage measure is adapted for selecting the smallest informative subset of
variables by reducing redundancy in data. This paper proposes a simple analogy
to apply this measure. It is implemented in a filter algorithm for unsupervised
feature selection problems.
  The proposed filter algorithm is robust with high dimensional data and can be
implemented without extra parameters. Further, it is tested with simulated data
and real world case studies including environmental data and hyperspectral
image. Finally, the results are evaluated by using random forest algorithm.
