TY - JOUR
T1 - Discriminative and Uncorrelated Feature Selection With Constrained Spectral Analysis in Unsupervised Learning
AU - Li, Xuelong
AU - Zhang, Han
AU - Zhang, Rui
AU - Nie, Feiping
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2020
Y1 - 2020
N2 - The existing unsupervised feature extraction methods frequently explore low-redundant features by an uncorrelated constraint. However, the constrained models might incur trivial solutions, due to the singularity of scatter matrix triggered by high-dimensional data. In this paper, we propose a regularized regression model with a generalized uncorrelated constraint for feature selection, which leads to three merits: 1) exploring the low-redundant and discriminative features; 2) avoiding the trivial solutions and 3) simplifying the optimization. Besides that, the local cluster structure is achieved via a novel constrained spectral analysis for the unsupervised learning, where Must-Links and Cannot-Links are transformed into a intrinsic graph and a penalty graph respectively, rather than incorporated into a mixed affinity graph. Accordingly, a discriminative and uncorrelated feature selection with constrained spectral analysis (DUCFS) is proposed with adopting σ-norm regularization for interpolating between F-norm and ℓ2,1-norm. Due to the flexible gradient and global differentiability, our model converges fast. Extensive experiments on benchmark datasets among several state-of-the-art approaches verify the effectiveness of the proposed method.
AB - The existing unsupervised feature extraction methods frequently explore low-redundant features by an uncorrelated constraint. However, the constrained models might incur trivial solutions, due to the singularity of scatter matrix triggered by high-dimensional data. In this paper, we propose a regularized regression model with a generalized uncorrelated constraint for feature selection, which leads to three merits: 1) exploring the low-redundant and discriminative features; 2) avoiding the trivial solutions and 3) simplifying the optimization. Besides that, the local cluster structure is achieved via a novel constrained spectral analysis for the unsupervised learning, where Must-Links and Cannot-Links are transformed into a intrinsic graph and a penalty graph respectively, rather than incorporated into a mixed affinity graph. Accordingly, a discriminative and uncorrelated feature selection with constrained spectral analysis (DUCFS) is proposed with adopting σ-norm regularization for interpolating between F-norm and ℓ2,1-norm. Due to the flexible gradient and global differentiability, our model converges fast. Extensive experiments on benchmark datasets among several state-of-the-art approaches verify the effectiveness of the proposed method.
KW - Discriminative and uncorrelated feature selection
KW - constrained spectral analysis
KW - generalized uncorrelated constraint
KW - relaxed regularization term
KW - unsupervised learning
UR - http://www.scopus.com/inward/record.url?scp=85077751733&partnerID=8YFLogxK
U2 - 10.1109/TIP.2019.2947776
DO - 10.1109/TIP.2019.2947776
M3 - 文章
C2 - 31670668
AN - SCOPUS:85077751733
SN - 1057-7149
VL - 29
SP - 2139
EP - 2149
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
IS - 1
M1 - 8884662
ER -