Pipe failure prediction: A data mining method
Rui Wang, Weishan Dong, et al.
ICDE 2013
In this brief, we propose a new max-margin-based discriminative feature learning method. In particular, we aim at learning a low-dimensional feature representation, so as to maximize the global margin of the data and make the samples from the same class as close as possible. In order to enhance the robustness to noise, we leverage a regularization term to make the transformation matrix sparse in rows. In addition, we further learn and leverage the correlations among multiple categories for assisting in learning discriminative features. The experimental results demonstrate the power of the proposed method against the related state-of-the-art methods.
Rui Wang, Weishan Dong, et al.
ICDE 2013
Changsheng Li, Fan Wei, et al.
AAAI 2016
Weishan Dong, Renjie Yao, et al.
CIKM 2014
Xin Zhang, Ning Duan, et al.
SOLI 2012