Unsupervised Feature Selection Via Orthogonal Basis Clustering and Local Structure Preserving

Lin, Xiaochang, Guan, Jiewen, Chen, Bilian and Zeng, Yifeng (2022) Unsupervised Feature Selection Via Orthogonal Basis Clustering and Local Structure Preserving. IEEE Transactions on Neural Networks and Learning Systems, 33 (11). pp. 6881-6892. ISSN 2162-237X

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Official URL: https://doi.org/10.1109/tnnls.2021.3083763


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.

Item Type: Article
Additional Information: Funding information: This work was supported in part by the National Natural Science Foundation of China under Grant 61836005 and Grant 61772442 and in part by the Youth Innovation Fund of Xiamen under Grant 3502Z20206049.
Uncontrolled Keywords: Manifolds, Sparse matrices, Symmetric matrices, Clustering algorithms, Optimization, Matrix decomposition, Feature extraction, Locality preserving, orthogonal basis clustering, theoretical analysis, unsupervised feature selection
Subjects: G500 Information Systems
G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Rachel Branson
Date Deposited: 01 Jul 2021 15:08
Last Modified: 04 Nov 2022 10:00
URI: https://nrl.northumbria.ac.uk/id/eprint/46581

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