Joint Adaptive Graph and Structured Sparsity Regularization for Unsupervised Feature Selection
This addresses the challenge of selecting optimal feature groups without manual tuning in unsupervised settings, which is incremental as it builds on existing manifold learning and sparsity techniques.
The paper tackles the problem of unsupervised feature selection by proposing a method that jointly learns adaptive graph structures and imposes structured sparsity regularization, resulting in automatic determination of feature numbers and improved performance on eight benchmarks compared to state-of-the-art approaches.
Feature selection is an important data preprocessing in data mining and machine learning which can be used to reduce the feature dimension without deteriorating model's performance. Since obtaining annotated data is laborious or even infeasible in many cases, unsupervised feature selection is more practical in reality. Though lots of methods for unsupervised feature selection have been proposed, these methods select features independently, thus it is no guarantee that the group of selected features is optimal. What's more, the number of selected features must be tuned carefully to obtain a satisfactory result. To tackle these problems, we propose a joint adaptive graph and structured sparsity regularization unsupervised feature selection (JASFS) method in this paper, in which a $l_{2,0}$-norm regularization term with respect to transformation matrix is imposed in the manifold learning for feature selection, and a graph regularization term is incorporated into the learning model to learn the local geometric structure of data adaptively. An efficient and simple iterative algorithm is designed to solve the proposed optimization problem with the analysis of computational complexity. After optimized, a subset of optimal features will be selected in group, and the number of selected features will be determined automatically. Experimental results on eight benchmarks demonstrate the effectiveness and efficiency of the proposed method compared with several state-of-the-art approaches.