TY - JOUR
T1 - Unsupervised Feature Selection via Orthogonal Basis Clustering and Local Structure Preserving
AU - Lin, Xiaochang
AU - Guan, Jiewen
AU - Chen, Bilian
AU - Zeng, Yifeng
N1 - Funding information: Research funded by National Natural Science Foundation of China (6183600561772442) | Youth Innovation Fund of Xiamen (3502Z20206049)
PY - 2022/11/1
Y1 - 2022/11/1
N2 - 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.
AB - 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.
KW - Manifolds
KW - Sparse matrices
KW - Symmetric matrices
KW - Clustering algorithms
KW - Optimization
KW - Matrix decomposition
KW - Feature extraction
KW - Locality preserving
KW - orthogonal basis clustering
KW - theoretical analysis
KW - unsupervised feature selection
KW - unsupervised feature selection.
UR - http://www.scopus.com/inward/record.url?scp=85111027822&partnerID=8YFLogxK
U2 - 10.1109/tnnls.2021.3083763
DO - 10.1109/tnnls.2021.3083763
M3 - Article
VL - 33
SP - 6881
EP - 6892
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
SN - 2162-237X
IS - 11
ER -