TY - GEN
T1 - Semi-supervised classification via both label and side information
AU - Zhang, Rui
AU - Nie, Feiping
AU - Li, Xuelong
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/16
Y1 - 2017/6/16
N2 - As for the semi-supervised learning, both label and side information serve as pretty significant indicators for the classification. However, majority of the associated works only focus on one side of the road. In other words, either the label information or the side information is utilized instead of taking both of them into consideration simultaneously. To address the referred defect, we propose a graph-based semi-supervised learning (GSL) problem via building the intrinsic graph and the penalty graph upon both label and side information. To efficiently unravel the proposed GSL problem, a novel quadratic trace ratio (QTR) method is proposed based on solving the associated QTR problem, which is the equivalent counterpart of the GSL problem. Besides, a parameter-free similarity is further derived and utilized. Consequently, a novel semi-supervised classification (SC) algorithm can be summarized by virtue of the proposed GSL problem and QTR method.
AB - As for the semi-supervised learning, both label and side information serve as pretty significant indicators for the classification. However, majority of the associated works only focus on one side of the road. In other words, either the label information or the side information is utilized instead of taking both of them into consideration simultaneously. To address the referred defect, we propose a graph-based semi-supervised learning (GSL) problem via building the intrinsic graph and the penalty graph upon both label and side information. To efficiently unravel the proposed GSL problem, a novel quadratic trace ratio (QTR) method is proposed based on solving the associated QTR problem, which is the equivalent counterpart of the GSL problem. Besides, a parameter-free similarity is further derived and utilized. Consequently, a novel semi-supervised classification (SC) algorithm can be summarized by virtue of the proposed GSL problem and QTR method.
KW - graph-based semi-supervised learning
KW - quadratic trace ratio problem
KW - side information
KW - soft label
UR - http://www.scopus.com/inward/record.url?scp=85023773763&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2017.7952590
DO - 10.1109/ICASSP.2017.7952590
M3 - 会议稿件
AN - SCOPUS:85023773763
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 2417
EP - 2421
BT - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Y2 - 5 March 2017 through 9 March 2017
ER -