Unsupervised adaptive floating search feature selection based on Contribution Entropy

In feature selection, a search problem of finding a subset of features from a given set of measurements has been of interest for a long time. However, unsupervised methods are scarce. Examples of unsupervised methods include using the variance of data collected for each feature, or the projection of the feature on the first principal component. Another unsupervised criterion, based on SVD-entropy (Singular Value Decomposition), selects a feature according to its contribution to the entropy (CE) calculated on a leave-one-out basis. Based on this criterion, this paper proposes an adaptive floating search feature selection method (AFS) with flexible backtracking capabilities. Features thus selected are evaluated using K-Means clustering algorithm. Experimental results show that the proposed method performs better in selecting an optimal size of the relevant feature set.