Self-representation and PCA embedding for unsupervised feature selection

Feature selection is an important preprocessing step for dealing with high dimensional data. In this paper, we propose a novel unsupervised feature selection method by embedding a subspace learning regularization (i.e., principal component analysis (PCA)) into the sparse feature selection framework. Specifically, we select informative features via the sparse learning framework and consider preserving the principal components (i.e., the maximal variance) of the data at the same time, such that improving the interpretable ability of the feature selection model. Furthermore, we propose an effective optimization algorithm to solve the proposed objective function which can achieve stable optimal result with fast convergence. By comparing with five state-of-the-art unsupervised feature selection methods on six benchmark and real-world datasets, our proposed method achieved the best result in terms of classification performance.