Robust Locality Preserving Projection Based on Kernel Risk-Sensitive Loss

Lei Xing; Yunqi Mi; Yuanhao Li; And Badong Chen

doi:10.1109/IJCNN.2018.8489225

Robust Locality Preserving Projection Based on Kernel Risk-Sensitive Loss

Lei Xing
, Yunqi Mi
, Yuanhao Li
, And Badong Chen

Faculty of Electronic and Information Engineering

Xi'an Jiaotong University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

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.

Original language	English
Title of host publication	2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509060146
DOIs	https://doi.org/10.1109/IJCNN.2018.8489225
State	Published - 10 Oct 2018
Event	2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil Duration: 8 Jul 2018 → 13 Jul 2018

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	2018-July

Conference

Conference	2018 International Joint Conference on Neural Networks, IJCNN 2018
Country/Territory	Brazil
City	Rio de Janeiro
Period	8/07/18 → 13/07/18

Keywords

kernel risk-sensitive loss (KRSL)
locality preserving projection (LPP)
robustness

Access to Document

10.1109/IJCNN.2018.8489225

Cite this

Xing, L., Mi, Y., Li, Y., & Chen, A. B. (2018). Robust Locality Preserving Projection Based on Kernel Risk-Sensitive Loss. In 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings Article 8489225 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2018.8489225

@inproceedings{0fc5dbc5b50745509d7e3c10dacc0aa5,

title = "Robust Locality Preserving Projection Based on Kernel Risk-Sensitive Loss",

abstract = "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.",

keywords = "kernel risk-sensitive loss (KRSL), locality preserving projection (LPP), robustness",

author = "Lei Xing and Yunqi Mi and Yuanhao Li and Chen, \{And Badong\}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 International Joint Conference on Neural Networks, IJCNN 2018 ; Conference date: 08-07-2018 Through 13-07-2018",

year = "2018",

month = oct,

day = "10",

doi = "10.1109/IJCNN.2018.8489225",

language = "英语",

series = "Proceedings of the International Joint Conference on Neural Networks",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings",

}

Xing, L, Mi, Y, Li, Y & Chen, AB 2018, Robust Locality Preserving Projection Based on Kernel Risk-Sensitive Loss. in 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings., 8489225, Proceedings of the International Joint Conference on Neural Networks, vol. 2018-July, Institute of Electrical and Electronics Engineers Inc., 2018 International Joint Conference on Neural Networks, IJCNN 2018, Rio de Janeiro, Brazil, 8/07/18. https://doi.org/10.1109/IJCNN.2018.8489225

Robust Locality Preserving Projection Based on Kernel Risk-Sensitive Loss. / Xing, Lei; Mi, Yunqi; Li, Yuanhao et al.
2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8489225 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2018-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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 -

Robust Locality Preserving Projection Based on Kernel Risk-Sensitive Loss

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this