Low-rank dictionary learning for unsupervised feature selection. (15th September 2022) - Parsa, Mohsen Ghassemi; Zare, Hadi; Ghatee, Mehdi
Abstract: There are many high-dimensional data in real-world applications such as biology, computer vision, and social networks. Feature selection approaches are devised to confront high-dimensional data challenges with the aim of efficient learning technologies as well as reduction of models complexity. Due to the hardship of labeling on these datasets, there are a variety of approaches for the feature selection process in an unsupervised setting by considering some important characteristics of data. In this paper, we introduce a novel unsupervised feature selection approach by applying dictionary learning idea in a low-rank representation. Low-rank dictionary learning not only enables us to provide a new data representation but also maintains feature correlation. Then, spectral analysis is employed to preserve sample similarities. Finally, a unified objective function for unsupervised feature selection is proposed in a sparse way by an ℓ 2, 1 -norm regularization. Furthermore, an efficient numerical algorithm is designed to solve the corresponding optimization problem. We demonstrate the performance of the proposed method based on a variety of standard datasets from different applied domains. Our experimental findings reveal that the proposed method outperforms the state-of-the-art algorithms. Highlights: Introduce a joint unsupervised feature selection by a low-rank dictionary learning approach. Apply spectral analysis to maintain sample similarities. Propose a unified feature selection in a regularized way. Experimental results are performed on a variety of applied domains. The experimental results reveal the strength of the proposed approach.