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Karimzadeh, Masoud, Moradi Dowlatabadi, Parham, Ghaderzadeh, Abdulbaghi. (1403). Unsupervised feature selection by integration of regularized self-representation and sparse coding. نشریه مهندسی عمران امیرکبیر, 5(2), 91-109. doi: 10.22060/ajmc.2023.21449.1090
Masoud Karimzadeh; Parham Moradi Dowlatabadi; Abdulbaghi Ghaderzadeh. "Unsupervised feature selection by integration of regularized self-representation and sparse coding". نشریه مهندسی عمران امیرکبیر, 5, 2, 1403, 91-109. doi: 10.22060/ajmc.2023.21449.1090
Karimzadeh, Masoud, Moradi Dowlatabadi, Parham, Ghaderzadeh, Abdulbaghi. (1403). 'Unsupervised feature selection by integration of regularized self-representation and sparse coding', نشریه مهندسی عمران امیرکبیر, 5(2), pp. 91-109. doi: 10.22060/ajmc.2023.21449.1090
Karimzadeh, Masoud, Moradi Dowlatabadi, Parham, Ghaderzadeh, Abdulbaghi. Unsupervised feature selection by integration of regularized self-representation and sparse coding. نشریه مهندسی عمران امیرکبیر, 1403; 5(2): 91-109. doi: 10.22060/ajmc.2023.21449.1090

Unsupervised feature selection by integration of regularized self-representation and sparse coding

AUT Journal of Mathematics and Computing
مقاله 2، دوره 5، شماره 2، 2024، صفحه 91-109    اصل مقاله (1.37 M)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22060/ajmc.2023.21449.1090
نویسندگان
Masoud Karimzadeh1؛ Parham Moradi Dowlatabadi* 2؛ Abdulbaghi Ghaderzadeh1
1Department of Computer Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran
2Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran
چکیده
Due to the development of social networks and the Internet of things, we recently have faced with large datasets. High-dimensional data is mixed with redundant and irrelevant features, so the performance of machine learning methods is reduced. Feature selection is a common way to tackle this issue with the aim of choosing a small subset of relevant and non-redundant features. Most of the existing feature selection works are for supervised applications, which assume that the information on class labels is available. While in many real-world applications, it is not possible to provide complete knowledge of class labels. To overcome this shortcoming, an unsupervised feature selection method is proposed in this paper. The proposed method uses the matrix factorization-based regularized self-representation model to weight features based on their importance. Here, we initialize the weights of features based on the correlation among features. Several experiments are performed to evaluate the effectiveness of the proposed method. Then the results are compared with several baselines and state-of-the-art methods, which show the superiority of the proposed method in most cases.
کلیدواژه‌ها
Unsupervised feature selection؛ Subspace learning؛ Self-representation
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