… in Unsupervised Feature Selection. To the best of our knowledge, this is the first comprehensive
review in Unsupervised Feature Selection … and recent feature selection methods in this …

… the computational time of feature selection include probabilistic … the article lies with
unsupervised feature selection, we discuss … These include sequential unsupervised feature …

… robustness for unsupervised feature selection. In light of all these factors, we propose a
new unsupervised feature selection algorithm, ie, Robust Unsupervised Feature Selection (RUFS…

… feature selection, which allows to embed feature selection into the … spare learning in
unsupervised feature selection. Due to the … unsupervised feature selection algorithm, ie, …

… Feature selection techniques are designed to find the relevant feature subset of the original
features … In this paper, we consider the feature selection problem in unsupervised learning …

… the bias of feature selection criteria with respect to dimension. We explore the feature selection
problem and these issues through FSSEM (Feature Subset Selection using Expectation-…

… methods to perform feature selection on unsupervised data. One can select a global set of
features or a local set. Global means that one selects a single subset of features that clusters …

… , unsupervised feature selection is a more difficult problem due to the unavailability of class
labels. In this paper, we present an unsupervised feature selection … the optimal feature subset …

… , we evaluate our main algorithm as an unsupervised feature selection strategy for PCA in
a … comparison instead of the unsupervised feature selection algorithms of Section 2 that opti…

In the past decade, various sparse learning based unsupervised feature selection methods
have been developed. However, most existing studies adopt a two-step strategy, ie, selecting …