Unsupervised Feature Selection Using Structured Self-Representation

Unsupervised feature selection has become an important and challenging problem faced with vast amounts of unlabeled and high-dimension data in machine learning. We propose a novel unsupervised feature selection method using Structured Self-Representation (SSR) by simultaneously taking into account the self-representation property and local geometrical structure of features. Concretely, according to the inherent self-representation property of features, the most representative features can be selected. Meanwhile, to obtain more accurate results, we explore local geometrical structure to constrain the representation coefficients to be close to each other if the features are close to each other. Furthermore, an efficient algorithm is presented for optimizing the objective function. Finally, experiments on the synthetic dataset and six benchmark real-world datasets, including biomedical data, letter recognition digit data and face image data, demonstrate the encouraging performance of the proposed algorithm compared with state-of-the-art algorithms.