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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
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ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 644266260
doi: 10.4316/AECE


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  2/2016 - 15
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Semi-Supervised Multi-View Ensemble Learning Based On Extracting Cross-View Correlation

ZALL, R. See more information about ZALL, R. on SCOPUS See more information about ZALL, R. on IEEExplore See more information about ZALL, R. on Web of Science, KEYVANPOUR, M. R. See more information about KEYVANPOUR, M. R. on SCOPUS See more information about KEYVANPOUR, M. R. on SCOPUS See more information about KEYVANPOUR, M. R. on Web of Science
 
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Download PDF pdficon (1,549 KB) | Citation | Downloads: 197 | Views: 363

Author keywords
boosting, correlation, classification algorithm, sampling methods, semi-supervised learning

References keywords
semi(17), supervised(16), learning(15), data(15), recognition(11), multi(10), analysis(10), zhang(9), view(9), pattern(9)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2016-05-31
Volume 16, Issue 2, Year 2016, On page(s): 111 - 124
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2016.02015
Web of Science Accession Number: 000376996100015
SCOPUS ID: 84974853415

Abstract
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Correlated information between different views incorporate useful for learning in multi view data. Canonical correlation analysis (CCA) plays important role to extract these information. However, CCA only extracts the correlated information between paired data and cannot preserve correlated information between within-class samples. In this paper, we propose a two-view semi-supervised learning method called semi-supervised random correlation ensemble base on spectral clustering (SS_RCE). SS_RCE uses a multi-view method based on spectral clustering which takes advantage of discriminative information in multiple views to estimate labeling information of unlabeled samples. In order to enhance discriminative power of CCA features, we incorporate the labeling information of both unlabeled and labeled samples into CCA. Then, we use random correlation between within-class samples from cross view to extract diverse correlated features for training component classifiers. Furthermore, we extend a general model namely SSMV_RCE to construct ensemble method to tackle semi-supervised learning in the presence of multiple views. Finally, we compare the proposed methods with existing multi-view feature extraction methods using multi-view semi-supervised ensembles. Experimental results on various multi-view data sets are presented to demonstrate the effectiveness of the proposed methods.


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References Weight

Web of Science® Citations for all references: 14,587 TCR
SCOPUS® Citations for all references: 11,578 TCR

Web of Science® Average Citations per reference: 310 ACR
SCOPUS® Average Citations per reference: 246 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2016-12-02 16:58 in 234 seconds.




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