TY - JOUR
T1 - Large-Scale Robust Semisupervised Classification
AU - Zhang, Lingling
AU - Luo, Minnan
AU - Li, Zhihui
AU - Nie, Feiping
AU - Zhang, Huaxiang
AU - Liu, Jun
AU - Zheng, Qinghua
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2019/3
Y1 - 2019/3
N2 - Semisupervised learning aims to leverage both labeled and unlabeled data to improve performance, where most of them are graph-based methods. However, the graph-based semisupervised methods are not capable for large-scale data since the computational consumption on the construction of graph Laplacian matrix is huge. On the other hand, the substantial unlabeled data in training stage of semisupervised learning could cause large uncertainties and potential threats. Therefore, it is crucial to enhance the robustness of semisupervised classification. In this paper, a novel large-scale robust semisupervised learning method is proposed in the framework of capped 2,p-norm. This strategy is superior not only in computational cost because it makes the graph Laplacian matrix unnecessary, but also in robustness to outliers since the capped 2,p-norm used for loss measurement. An efficient optimization algorithm is exploited to solve the nonconvex and nonsmooth challenging problem. The complexity of the proposed algorithm is analyzed and discussed in theory detailedly. Finally, extensive experiments are conducted over six benchmark data sets to demonstrate the effectiveness and superiority of the proposed method.
AB - Semisupervised learning aims to leverage both labeled and unlabeled data to improve performance, where most of them are graph-based methods. However, the graph-based semisupervised methods are not capable for large-scale data since the computational consumption on the construction of graph Laplacian matrix is huge. On the other hand, the substantial unlabeled data in training stage of semisupervised learning could cause large uncertainties and potential threats. Therefore, it is crucial to enhance the robustness of semisupervised classification. In this paper, a novel large-scale robust semisupervised learning method is proposed in the framework of capped 2,p-norm. This strategy is superior not only in computational cost because it makes the graph Laplacian matrix unnecessary, but also in robustness to outliers since the capped 2,p-norm used for loss measurement. An efficient optimization algorithm is exploited to solve the nonconvex and nonsmooth challenging problem. The complexity of the proposed algorithm is analyzed and discussed in theory detailedly. Finally, extensive experiments are conducted over six benchmark data sets to demonstrate the effectiveness and superiority of the proposed method.
KW - Classification
KW - ridge regression
KW - robustness
KW - semisupervised learning
UR - http://www.scopus.com/inward/record.url?scp=85040911950&partnerID=8YFLogxK
U2 - 10.1109/TCYB.2018.2789420
DO - 10.1109/TCYB.2018.2789420
M3 - 文章
C2 - 29994188
AN - SCOPUS:85040911950
SN - 2168-2267
VL - 49
SP - 907
EP - 917
JO - IEEE Transactions on Cybernetics
JF - IEEE Transactions on Cybernetics
IS - 3
M1 - 8262645
ER -