Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 202, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2022.117149
Keywords
Unsupervised feature selection; Dictionary learning; Sparse learning; Spectral analysis; Low-rank representation
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This paper introduces a novel unsupervised feature selection approach, which utilizes low-rank representation with dictionary learning and spectral analysis. By proposing a unified objective function and efficient algorithm, the method outperforms existing algorithms on various standard datasets.
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 l2,1-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.
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