Feature weighting as a tool for unsupervised feature selection

Deepak Panday, Renato Cordeiro De Amorim, Peter Lane

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)
58 Downloads (Pure)


Feature selection is a popular data pre-processing step. The aim is to remove some of the features in a data set with minimum information loss, leading to a number of benefits including faster running time and easier data visualisation.
In this paper we introduce two unsupervised feature selection algorithms. These make use of a cluster-dependent feature-weighting mechanism reflecting the within-cluster degree of relevance of a given feature. Those features with
a relatively low weight are removed from the data set. We compare our algorithms to two other popular alternatives using a number of experiments on both synthetic and real-world data sets, with and without added noisy features.
These experiments demonstrate our algorithms clearly outperform the alternatives.
Original languageEnglish
Pages (from-to)44-52
Number of pages9
JournalInformation Processing Letters
Early online date21 Sept 2017
Publication statusPublished - 1 Jan 2018


  • Algorithms
  • Clustering
  • Feature selection


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