Feature Selection for Unsupervised Machine Learning.

Compared to supervised machine learning (ML), the development of feature selection for unsupervised ML is far behind. To address this issue, the current research proposes a stepwise feature selection approach for clustering methods with a specification to the Gaussian mixture model (GMM) and the $k$-means. Rather than the existing GMM and $k$-means which are carried out based on all the features, the proposed method selects a subset of features to implement the two methods, respectively. The research finds that a better result can be obtained if the existing GMM and $k$-means methods are modified by nice initializations. Experiments based on Monte Carlo simulations show that the proposed method is more computationally efficient and the result is more accurate than the existing GMM and $k$-means methods based on all the features. The experiment based on a real-world dataset confirms this finding.