Unsupervised Feature Selection via Orthogonal Basis Clustering and Local Structure Preserving
| العنوان: | Unsupervised Feature Selection via Orthogonal Basis Clustering and Local Structure Preserving |
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| المؤلفون: | Xiaochang Lin, Bilian Chen, Yifeng Zeng, Jiewen Guan |
| المصدر: | IEEE Transactions on Neural Networks and Learning Systems. 33:6881-6892 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Optimization problem, Computational complexity theory, Artificial neural network, G500, Computer Networks and Communications, Computer science, business.industry, Feature extraction, G900, Feature selection, Pattern recognition, Orthogonal basis, Computer Science Applications, Artificial Intelligence, Artificial intelligence, business, Cluster analysis, Software, Curse of dimensionality |
| الوصف: | Due to the ``curse of dimensionality'' issue, how to discard redundant features and select informative features in high-dimensional data has become a critical problem, hence there are many research studies dedicated to solving this problem. Unsupervised feature selection technique, which does not require any prior category information to conduct with, has gained a prominent place in preprocessing high-dimensional data among all feature selection techniques, and it has been applied to many neural networks and learning systems related applications, e.g., pattern classification. In this article, we propose an efficient method for unsupervised feature selection via orthogonal basis clustering and reliable local structure preserving, which is referred to as OCLSP briefly. Our OCLSP method consists of an orthogonal basis clustering together with an adaptive graph regularization, which realizes the functionality of simultaneously achieving excellent cluster separation and preserving the local information of data. Besides, we exploit an efficient alternative optimization algorithm to solve the challenging optimization problem of our proposed OCLSP method, and we perform a theoretical analysis of its computational complexity and convergence. Eventually, we conduct comprehensive experiments on nine real-world datasets to test the validity of our proposed OCLSP method, and the experimental results demonstrate that our proposed OCLSP method outperforms many state-of-the-art unsupervised feature selection methods in terms of clustering accuracy and normalized mutual information, which indicates that our proposed OCLSP method has a strong ability in identifying more important features. |
| وصف الملف: | application/pdf |
| تدمد: | 2162-2388 2162-237X |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dfebf3a79c2fa1e84ce7b4919f766157 https://doi.org/10.1109/tnnls.2021.3083763 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....dfebf3a79c2fa1e84ce7b4919f766157 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 21622388 2162237X |
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