TY - JOUR
T1 - Adaptive discriminant analysis for semi-supervised feature selection
AU - Zhong, Weichan
AU - Chen, Xiaojun
AU - Nie, Feiping
AU - Huang, Joshua Zhexue
N1 - Publisher Copyright:
© 2021 Elsevier Inc.
PY - 2021/8
Y1 - 2021/8
N2 - As semi-supervised feature selection is becoming much more popular among researchers, many related methods have been proposed in recent years. However, many of these methods first compute a similarity matrix prior to feature selection, and the matrix is then fixed during the subsequent feature selection process. Clearly, the similarity matrix generated from the original dataset is susceptible to the noise features. In this paper, we propose a novel adaptive discriminant analysis for semi-supervised feature selection, namely, SADA. Instead of computing a similarity matrix first, SADA simultaneously learns an adaptive similarity matrix S and a projection matrix W with an iterative process. Moreover. we introduce the ℓ2,p norm to control the sparsity of S by adjusting p. Experimental results show that S will become sparser with the decrease of p. The experimental results for synthetic datasets and nine benchmark datasets demonstrate the superiority of SADA, in comparison with 6 semi-supervised feature selection methods.
AB - As semi-supervised feature selection is becoming much more popular among researchers, many related methods have been proposed in recent years. However, many of these methods first compute a similarity matrix prior to feature selection, and the matrix is then fixed during the subsequent feature selection process. Clearly, the similarity matrix generated from the original dataset is susceptible to the noise features. In this paper, we propose a novel adaptive discriminant analysis for semi-supervised feature selection, namely, SADA. Instead of computing a similarity matrix first, SADA simultaneously learns an adaptive similarity matrix S and a projection matrix W with an iterative process. Moreover. we introduce the ℓ2,p norm to control the sparsity of S by adjusting p. Experimental results show that S will become sparser with the decrease of p. The experimental results for synthetic datasets and nine benchmark datasets demonstrate the superiority of SADA, in comparison with 6 semi-supervised feature selection methods.
KW - Discriminant analysis
KW - Feature selection
KW - Semi-supervised feature selection
UR - http://www.scopus.com/inward/record.url?scp=85103424330&partnerID=8YFLogxK
U2 - 10.1016/j.ins.2021.02.035
DO - 10.1016/j.ins.2021.02.035
M3 - 文章
AN - SCOPUS:85103424330
SN - 0020-0255
VL - 566
SP - 178
EP - 194
JO - Information Sciences
JF - Information Sciences
ER -