DocumentCode
2185263
Title
Lp norm spectral regression for feature extraction in outlier conditions
Author
Zhou, Weiwei ; Li, Peiyang ; Wang, Xurui ; Li, Fali ; Liu, Huan ; Zhang, Rui ; Ma, Teng ; Liu, Tiejun ; Guo, Daqing ; Yao, Dezhong ; Xu, Peng
Author_Institution
Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, ChengDu, 610054, China
fYear
2015
fDate
21-24 July 2015
Firstpage
535
Lastpage
538
Abstract
Spectral regression is a newly proposed method which is widely used in signal processing and feature extraction. However, like most methods based on regression analysis, it is prone to outlier artifacts with large norm. In this paper, a novel regression function for SR is constructed in the Lp (p ≤ 1) norm space with the aim at compressing the outlier effects on pattern recognition. The quantitative evaluation using simulated outliers demonstrates the proposed method can effectively deal with the outliers introduced in the features.
Keywords
Eigenvalues and eigenfunctions; Electroencephalography; Feature extraction; Kernel; Pattern recognition; Robustness; Signal processing; dimensional reduction; lp norm; outlier; pattern recognition; spectral regression;
fLanguage
English
Publisher
ieee
Conference_Titel
Digital Signal Processing (DSP), 2015 IEEE International Conference on
Conference_Location
Singapore, Singapore
Type
conf
DOI
10.1109/ICDSP.2015.7251930
Filename
7251930
Link To Document