TY - JOUR
T1 - Fast orthogonal locality-preserving projections for unsupervised feature selection
AU - Zhu, Jianyong
AU - Chen, Jingwei
AU - Xu, Bin
AU - Yang, Hui
AU - Nie, Feiping
N1 - Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2023/4/28
Y1 - 2023/4/28
N2 - Graph-based sparsity learning is one of the most successful unsupervised feature selection methods that has been widely adopted in many real-world applications. However, traditional graph-based unsupervised feature selection methods have several drawbacks: (1) being time-consuming and unable to deal with large-scale problems; (2) having difficulty tuning the regularization parameter with the sparsity regularization term; and (3) being unable to find explicit solutions owing to the limitation of sparsity, that is, feature selection with the ℓ2,1-norm constrained problem. Thus, this paper proposes OLPPFS, a method to preserve the local geometric structure within the feature subspace by imposing the ℓ2,0-norm constraint. First, the linear mapping capability of the proposed model is enhanced using locality-preserving projections (LPPs), whichpreserve the local and global geometric manifold structure of the data while enhancing the ability to reconstruct data. Second, the graph-embedding learning method can accelerate the construction of a sparsity affinity graph and describe the intrinsic structure of the dataset well. More importantly, we propose a method for solving a projection matrix with the ℓ2,0-norm constrained, which can accurately select a explicit group of discriminative feature subsets. This method can yield a more accurate sparse projection matrix than the ℓ2,1-norm. We also adopt FOLPPFS, an effective anchor-based strategy to further accelerate our model with two flexible options. Extensive experiments on eight datasets demonstrate that the proposed method is superior to the other methods and can preserve a better local geometric structure of the dataset with less time consumption.
AB - Graph-based sparsity learning is one of the most successful unsupervised feature selection methods that has been widely adopted in many real-world applications. However, traditional graph-based unsupervised feature selection methods have several drawbacks: (1) being time-consuming and unable to deal with large-scale problems; (2) having difficulty tuning the regularization parameter with the sparsity regularization term; and (3) being unable to find explicit solutions owing to the limitation of sparsity, that is, feature selection with the ℓ2,1-norm constrained problem. Thus, this paper proposes OLPPFS, a method to preserve the local geometric structure within the feature subspace by imposing the ℓ2,0-norm constraint. First, the linear mapping capability of the proposed model is enhanced using locality-preserving projections (LPPs), whichpreserve the local and global geometric manifold structure of the data while enhancing the ability to reconstruct data. Second, the graph-embedding learning method can accelerate the construction of a sparsity affinity graph and describe the intrinsic structure of the dataset well. More importantly, we propose a method for solving a projection matrix with the ℓ2,0-norm constrained, which can accurately select a explicit group of discriminative feature subsets. This method can yield a more accurate sparse projection matrix than the ℓ2,1-norm. We also adopt FOLPPFS, an effective anchor-based strategy to further accelerate our model with two flexible options. Extensive experiments on eight datasets demonstrate that the proposed method is superior to the other methods and can preserve a better local geometric structure of the dataset with less time consumption.
KW - Graph theory
KW - Orthogonal locality-preserving projections
KW - Unsupervised feature selection
KW - ℓ-norm
UR - http://www.scopus.com/inward/record.url?scp=85148334789&partnerID=8YFLogxK
U2 - 10.1016/j.neucom.2023.02.021
DO - 10.1016/j.neucom.2023.02.021
M3 - 文章
AN - SCOPUS:85148334789
SN - 0925-2312
VL - 531
SP - 100
EP - 113
JO - Neurocomputing
JF - Neurocomputing
ER -