TY - GEN
T1 - Semi-Supervised Top-k Feature Selection with a General Optimization Framework
AU - Xu, Lei
AU - Wang, Rong
AU - Nie, Feiping
AU - Wu, Jun
AU - Li, Xuelong
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Feature selection is widely used in multimedia applications to determine informative features from high-dimensional data. Due to the explosive growth of the data size and the expensive cost of obtaining labeled data, it is increasingly demanded to utilize both labeled and unlabeled data for feature selection. In this paper, we introduce the l2,0-norm in semi-supervised feature selection, which is able to select exact k informative features. Due to the non-convexity of l2,0-norm, we further devise an efficient coordinate-descent-based algorithm to solve the l2,0-norm constraint, which facilitates the application of l2,0-norm to more complex applications, including but not limited to the proposed model in this study. We experimentally verify the effectiveness of the proposed l2,0-norm-based semi-supervised method and the efficiency of the proposed optimization algorithm.
AB - Feature selection is widely used in multimedia applications to determine informative features from high-dimensional data. Due to the explosive growth of the data size and the expensive cost of obtaining labeled data, it is increasingly demanded to utilize both labeled and unlabeled data for feature selection. In this paper, we introduce the l2,0-norm in semi-supervised feature selection, which is able to select exact k informative features. Due to the non-convexity of l2,0-norm, we further devise an efficient coordinate-descent-based algorithm to solve the l2,0-norm constraint, which facilitates the application of l2,0-norm to more complex applications, including but not limited to the proposed model in this study. We experimentally verify the effectiveness of the proposed l2,0-norm-based semi-supervised method and the efficiency of the proposed optimization algorithm.
KW - feature selection
KW - l -norm constraint
KW - semi-supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85171175855&partnerID=8YFLogxK
U2 - 10.1109/ICME55011.2023.00057
DO - 10.1109/ICME55011.2023.00057
M3 - 会议稿件
AN - SCOPUS:85171175855
T3 - Proceedings - IEEE International Conference on Multimedia and Expo
SP - 288
EP - 293
BT - Proceedings - 2023 IEEE International Conference on Multimedia and Expo, ICME 2023
PB - IEEE Computer Society
T2 - 2023 IEEE International Conference on Multimedia and Expo, ICME 2023
Y2 - 10 July 2023 through 14 July 2023
ER -