Low-rank dictionary learning for unsupervised feature selection. (15th September 2022)

• Record Type: Journal Article
• Authors: Parsa, Mohsen Ghassemi; Zare, Hadi; Ghatee, Mehdi
• Is Part Of: Expert systems with applications. Volume 202(2022)
• Publication Date: 2022-09-15
• Subjects: Unsupervised feature selection -- Dictionary learning -- Sparse learning -- Spectral analysis -- Low-rank representation; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.117149
• Languages: English
• ISSNs: 0957-4174

Abstract

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.