TY - GEN
T1 - Robust Locality Preserving Projection Based on Kernel Risk-Sensitive Loss
AU - Xing, Lei
AU - Mi, Yunqi
AU - Li, Yuanhao
AU - Chen, And Badong
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/10
Y1 - 2018/10/10
N2 - Traditional locality preserving projection (LPP) is an excellent linear dimensionality reduction method that can preserve the local structure of the data. The objective function of LPP is based on L2-norm criterion, which results in obvious sensitivity to the outliers. In order to solve this problem, researchers proposed some LPP variants based on the L1-norm (LPP-L1) and the maximum correntropy criterion (LPP-MCC). In this paper, we propose a more robust version of LPP, called LPP-KRSL, whose objective function is based on the kernel risk-sensitive loss (KRSL). The objective function can be efficiently solved via a half-quadratic optimization procedure. The experimental results on both synthetic and real-world data demonstrate that LPP-KRSL is more robust and effective than other LPP methods.
AB - Traditional locality preserving projection (LPP) is an excellent linear dimensionality reduction method that can preserve the local structure of the data. The objective function of LPP is based on L2-norm criterion, which results in obvious sensitivity to the outliers. In order to solve this problem, researchers proposed some LPP variants based on the L1-norm (LPP-L1) and the maximum correntropy criterion (LPP-MCC). In this paper, we propose a more robust version of LPP, called LPP-KRSL, whose objective function is based on the kernel risk-sensitive loss (KRSL). The objective function can be efficiently solved via a half-quadratic optimization procedure. The experimental results on both synthetic and real-world data demonstrate that LPP-KRSL is more robust and effective than other LPP methods.
KW - kernel risk-sensitive loss (KRSL)
KW - locality preserving projection (LPP)
KW - robustness
UR - https://www.scopus.com/pages/publications/85056528081
U2 - 10.1109/IJCNN.2018.8489225
DO - 10.1109/IJCNN.2018.8489225
M3 - 会议稿件
AN - SCOPUS:85056528081
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Joint Conference on Neural Networks, IJCNN 2018
Y2 - 8 July 2018 through 13 July 2018
ER -