Multiview correlation feature learning with multiple kernels

Yuan, Yun-Hao, Shen, Xiao-Bo, Xiao, Zhi-Yong, Yang, Jin-Long, Ge, Hong-Wei and Sun, Quan-Sen (2015). Multiview correlation feature learning with multiple kernels. In: Xiaofei He, Xinbo Gao, Yanning Zhang, Zhi-Hua Zhou, Zhi-Yong Liu, Baochuan Fu, Fuyuan Hu and Zhancheng Zhang, Intelligence Science and Big Data Engineering: Big Data and Machine Learning Techniques. 5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015, Suzhou, China, (518-528). 14-16 June 2015. doi:10.1007/978-3-319-23862-3_51

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads

Author Yuan, Yun-Hao
Shen, Xiao-Bo
Xiao, Zhi-Yong
Yang, Jin-Long
Ge, Hong-Wei
Sun, Quan-Sen
Title of paper Multiview correlation feature learning with multiple kernels
Conference name 5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015
Conference location Suzhou, China
Conference dates 14-16 June 2015
Convener Zhang, Yanning
Proceedings title Intelligence Science and Big Data Engineering: Big Data and Machine Learning Techniques   Check publisher's open access policy
Journal name Lecture Notes in Computer Science   Check publisher's open access policy
Series Lecture Notes in Computer Science
Place of Publication Cham, Switzerland
Publisher Springer
Publication Year 2015
Year available 2015
Sub-type Fully published paper
DOI 10.1007/978-3-319-23862-3_51
Open Access Status Not Open Access
ISBN 9783319238616
9783319238623
ISSN 1611-3349
0302-9743
Editor Xiaofei He
Xinbo Gao
Yanning Zhang
Zhi-Hua Zhou
Zhi-Yong Liu
Baochuan Fu
Fuyuan Hu
Zhancheng Zhang
Volume 9243
Start page 518
End page 528
Total pages 11
Chapter number 51
Total chapters 62
Language eng
Abstract/Summary Recent researches have shown the necessity to consider multiple kernels rather than a single fixed kernel in real-world applications. The learning performance can be significantly improved if multiple kernel functions or kernel matrices are considered. Motivated by the recent progress, in this paper we present a multiple kernel multiview correlation feature learning method for multiview dimensionality reduction. In our proposed method, the input data of each view are mapped into multiple higher dimensional feature spaces by implicitly nonlinear mappings. Three experiments on face and handwritten digit recognition have demonstrated the effectiveness of the proposed method.
Subjects 2614 Theoretical Computer Science
1700 Computer Science
Keyword Image recognition
Multiple kernels
Canonical correlation analysis
Multiset canonical correlations
Multiview feature learning
Q-Index Code E1
Q-Index Status Confirmed Code
Grant ID 61273251
61305017
61402203
Institutional Status UQ

Document type: Conference Paper
Sub-type: Fully published paper
Collections: Official 2016 Collection
School of Information Technology and Electrical Engineering Publications
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in Thomson Reuters Web of Science Article
Scopus Citation Count Cited 0 times in Scopus Article
Google Scholar Search Google Scholar
Created: Tue, 17 Nov 2015, 13:32:53 EST by System User on behalf of School of Information Technol and Elec Engineering