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

Unsupervised Feature Selection Based on Space Filling Concept