Unsupervised feature selection based on decision graph

In applications of algorithms, feature selection has got much attention of researchers, due to its ability to overcome the curse of dimensionality, reduce computational costs, increase the performance of the subsequent classification algorithm and output the results with better interpretability. To remove the redundant and noisy features from original feature set, we define local density and discriminant distance for each feature vector, wherein local density is used for measuring the representative ability of each feature vector, and discriminant distance is used for measuring the redundancy and similarity between features. Based on the above two quantities, the decision graph score is proposed as the evaluation criterion of unsupervised feature selection. The method is intuitive and simple, and its performances are evaluated in the data classification experiments. From statistical tests on the averaged classification accuracies over 16 real-life dataset, it is observed that the proposed method obtains better or comparable ability of discriminant feature selection in 98% of the cases, compared with the state-of-the-art methods.