Unsupervised feature selection based on adaptive similarity learning and subspace clustering. (October 2020)

• Record Type: Journal Article
• Authors: Parsa, Mohsen Ghassemi; Zare, Hadi; Ghatee, Mehdi
• Is Part Of: Engineering applications of artificial intelligence. Volume 95(2020)
• Publication Date: 2020-10
• Subjects: Unsupervised feature selection -- Graph learning -- Subspace clustering -- Sparse learning -- Representation learning; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2020.103855
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Feature selection methods have an important role on the readability of data and the reduction of complexity of learning algorithms. In recent years, a variety of efforts are investigated on feature selection problems based on unsupervised viewpoint due to the laborious labeling task on large datasets. In this paper, we propose a novel approach on unsupervised feature selection initiated from the subspace clustering to preserve the similarities by representation learning of low dimensional subspaces among the samples. A self-expressive model is employed to implicitly learn the cluster similarities in an adaptive manner. The proposed method not only maintains the sample similarities through subspace clustering, but it also considers the underlying structure of data based on a regularized regression model. In line with the convergence analysis of the proposed method, the experimental results on benchmark datasets demonstrate the effectiveness of our approach as compared with the state-of-the-art methods. Highlights: Propose a novel unsupervised feature selection method based on subspace clustering and adaptive similarity learning. Introduce a self-expressive model to adaptively and implicitly learn the cluster similarities. Employ a regularized regression model to obtain the sparse correlation among the features and clusters. Introduce a unified objective function to consider the main factors in the proposed approach. The results show that our proposed approach outperformed several state-of-the-art methods.