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

Document Type : Original Article

Authors

1 Department of Computer Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran

2 Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran

Abstract

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.

Keywords

References

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AUT Journal of Mathematics and Computing
Volume 5, Issue 2
April 2024
Pages 91-109

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