TY - JOUR
T1 - Structured Graph Optimization for Unsupervised Feature Selection
AU - Nie, Feiping
AU - Zhu, Wei
AU - Li, Xuelong
N1 - Publisher Copyright:
© 1989-2012 IEEE.
PY - 2021/3/1
Y1 - 2021/3/1
N2 - Unsupervised feature selection has attracted more and more attention due to the rapid growth of the large amount of unlabelled and high-dimensional data. The performance of traditional spectral-based unsupervised methods always depends on the quality of constructed similarity matrix. However, real world data always contain a large number of noise samples and features that make the similarity matrix created by original data cannot be fully relied. We propose an unsupervised feature selection method which conducts feature selection and local structure learning simultaneously. Moreover, we add an important constraint on the similarity matrix to allow it to capture more accurate information of the data structure. To perform feature selection, orthogonal constraint and $\ell _{2,p}$ℓ2,p-norm are adopted on the projection matrix. An efficient and simple algorithm is derived to tackle the problem. We conduct comprehensive experiments on various benchmark data sets, including handwritten digit, face image, and biomedical data, to validate the effectiveness of the proposed approach.
AB - Unsupervised feature selection has attracted more and more attention due to the rapid growth of the large amount of unlabelled and high-dimensional data. The performance of traditional spectral-based unsupervised methods always depends on the quality of constructed similarity matrix. However, real world data always contain a large number of noise samples and features that make the similarity matrix created by original data cannot be fully relied. We propose an unsupervised feature selection method which conducts feature selection and local structure learning simultaneously. Moreover, we add an important constraint on the similarity matrix to allow it to capture more accurate information of the data structure. To perform feature selection, orthogonal constraint and $\ell _{2,p}$ℓ2,p-norm are adopted on the projection matrix. An efficient and simple algorithm is derived to tackle the problem. We conduct comprehensive experiments on various benchmark data sets, including handwritten digit, face image, and biomedical data, to validate the effectiveness of the proposed approach.
KW - Structured optimal graph
KW - embedded method
KW - manifold learning
KW - unsupervised feature selection
UR - http://www.scopus.com/inward/record.url?scp=85100599053&partnerID=8YFLogxK
U2 - 10.1109/TKDE.2019.2937924
DO - 10.1109/TKDE.2019.2937924
M3 - 文章
AN - SCOPUS:85100599053
SN - 1041-4347
VL - 33
SP - 1210
EP - 1222
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
IS - 3
M1 - 8815854
ER -