TY - JOUR
T1 - Weighted co-association rate-based Laplacian regularized label description for semi-supervised regression
AU - Yu, Jaehong
AU - Son, Youngdoo
N1 - Publisher Copyright:
© 2020 Elsevier Inc.
PY - 2021/2/4
Y1 - 2021/2/4
N2 - Smoothness regularization derives the optimal regression function by minimizing the squared loss combined with a smoothness regularizer that restricts the variation of the function within a neighboring region. Thus, the regression function can effectively accommodate intrinsic data structures, and prediction performance can be improved when the label information is insufficient. In this study, we propose a weighted co-association rate-based Laplacian regularized label description algorithm. In the proposed algorithm, we define a regression function by combining weighted co-association rates and a label descriptive function. We use the weighted co-association rate, computed by summarizing various clustering solutions, to depict the data structure. The label descriptive function identifies a latent label distribution, and hence helps the regression function to accurately involve as much true label information as possible. To derive the optimal label descriptive function, we apply the smoothness regularizer to label descriptive function. Experiments were conducted on various benchmark datasets to examine the properties of the proposed algorithms, and the results were compared with those of the existing methods. The experimental results confirm that the proposed algorithm outperforms the previous methods.
AB - Smoothness regularization derives the optimal regression function by minimizing the squared loss combined with a smoothness regularizer that restricts the variation of the function within a neighboring region. Thus, the regression function can effectively accommodate intrinsic data structures, and prediction performance can be improved when the label information is insufficient. In this study, we propose a weighted co-association rate-based Laplacian regularized label description algorithm. In the proposed algorithm, we define a regression function by combining weighted co-association rates and a label descriptive function. We use the weighted co-association rate, computed by summarizing various clustering solutions, to depict the data structure. The label descriptive function identifies a latent label distribution, and hence helps the regression function to accurately involve as much true label information as possible. To derive the optimal label descriptive function, we apply the smoothness regularizer to label descriptive function. Experiments were conducted on various benchmark datasets to examine the properties of the proposed algorithms, and the results were compared with those of the existing methods. The experimental results confirm that the proposed algorithm outperforms the previous methods.
KW - Label descriptive function
KW - Semi-supervised regression
KW - Smoothness regularization
KW - Weighted co-association rate
UR - http://www.scopus.com/inward/record.url?scp=85092127700&partnerID=8YFLogxK
U2 - 10.1016/j.ins.2020.09.015
DO - 10.1016/j.ins.2020.09.015
M3 - Article
AN - SCOPUS:85092127700
SN - 0020-0255
VL - 545
SP - 688
EP - 712
JO - Information Sciences
JF - Information Sciences
ER -