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Semi-Supervised Multi-View Ensemble Learning Based On Extracting Cross-View CorrelationZALL, R. , KEYVANPOUR, M. R.
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boosting, correlation, classification algorithm, sampling methods, semi-supervised learning
semi(17), supervised(16), learning(15), data(15), recognition(11), multi(10), analysis(10), view(9), pattern(9), training(7)
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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
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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