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Print ISSN: 1582-7445
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doi: 10.4316/AECE


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  2/2016 - 15
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 HIGHLY CITED PAPER 

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: 924 | Views: 3,140

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), view(9), pattern(9), training(7)
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.


References | Cited By  «-- Click to see who has cited this paper

[1] Precup, R. E., David, R. C., Petriu, E. M., Preitl, S., and Radac, M. B. "Fuzzy logic-based adaptive gravitational search algorithm for optimal tuning of fuzzy-controlled servo systems." Control Theory & Applications, Iet 7.1 (2013): 99-107.
[CrossRef] [Web of Science Times Cited 67] [SCOPUS Times Cited 77]


[2] El Sehiemy, Ragab, Adel Abou El-Ela, and AbdulAllah Shaheen. "Multi-objective fuzzy-based procedure for enhancing reactive power management." Generation, Transmission & Distribution, IET 7.12 (2013): 1453-1460.
[CrossRef] [Web of Science Times Cited 28] [SCOPUS Times Cited 38]


[3] Kazakov, Alexander L., and Anna A. Lempert. "On Mathematical Models for Optimization Problem of Logistics Infrastructure." International Journal of Artificial Intelligence™ 13.1 (2015): 200-210.

[4] Danubianu, M., and St Gh Pentiuc. "Data Dimensionality Reduction Framework for Data Mining." Elektronika ir Elektrotechnika 19.4 (2013): 87-90.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 1]


[5] Gacto, María Jose, Gacto, M. J., Galende, M., Alcalá, R., and Herrera, F.. "Metsk-hd e: A multi objective evolutionary algorithm to learn accurate tsk-fuzzy systems in high-dimensional and large-scale regression problems." Information Sciences 276 (2014): 63-79.
[CrossRef] [Web of Science Times Cited 61] [SCOPUS Times Cited 63]


[6] X. Shen , Q. Sun, "A novel semi-supervised canonical correlation analysis and extensions for multi-view dimensionality reduction" Journal of Visual Communication and Image Representation, vol. 25, no. 8, pp. 1894-1904, 2014.
[CrossRef] [Web of Science Times Cited 34] [SCOPUS Times Cited 39]


[7] A. Blum, T. Mitchell, "Combining labeled and unlabeled data with co-training," Proceedings of the 11th annual conference on computational learning theory, p. 92-100, 1998.
[CrossRef] [SCOPUS Times Cited 4427]


[8] N. Kamal , R. Ghani, "Analyzing the effectiveness and applicability of co-training" Proceedings of the 9th international conference on information and knowledge management, p. 86-93, 2000.
[CrossRef] [SCOPUS Times Cited 848]


[9] J. Zhang, D. Zhang, "A novel ensemble construction method for multi-view data using random cross-view correlation between within-class examples," Pattern Recognition, vol. 44, no. 6, pp. 1162-1171, 2011.
[CrossRef] [Web of Science Times Cited 26] [SCOPUS Times Cited 29]


[10] H. Hotelling, "Relations between two sets of variates," Biometrika, vol. 28, p. 321-377, 1936.

[11] T. Sun, S. Chen, J. Yang, P. Shi, "A novel method of combined feature extraction for recognition," in: Eighth IEEE International Conference on Data Mining, ICDM'08 IEEE, p. 1043-1048, 2008.
[CrossRef] [Web of Science Times Cited 116] [SCOPUS Times Cited 163]


[12] Q. S. Sun, Z. D. Liu, P. A. Heng, D. S. Xia, " A theorem on the generalized canonical projective vectors," Pattern Recognition, vol. 38 , p. 449-452, 2005.
[CrossRef] [Web of Science Times Cited 65] [SCOPUS Times Cited 79]


[13] Y. Peng, D. Zhang, J. Zhang, "A new canonical correlation analysis algorithm with local discrimination," Neural Processing Letter, vol. 31, 2010.
[CrossRef] [Web of Science Times Cited 64] [SCOPUS Times Cited 77]


[14] N. Guan, X. Zhang, Z. Luo, L. Lan, "Sparse representation based discriminative canonical correlation analysis for face recognition," in: 2012 IEEE 11th International Conference on Machine Learning and Applications (ICMLA), p. 51-56, 2012.
[CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 20]


[15] X. Chen, S. Chen, H. Xue, X. Zhoun, "A unified dimensionality reduction framework for semi-paired and semi-supervised multi-view data" Pattern Recognition, vol. 45, 2012.
[CrossRef] [Web of Science Times Cited 57] [SCOPUS Times Cited 69]


[16] Y. Peng, D. Zhang, "Semi-supervised canonical correlation analysis algorithm" J.Sorftw, vol. 19, 2008.
[CrossRef] [SCOPUS Times Cited 33]


[17] Z.-H. Zhou, "When semi-supervised learning meets ensemble learning" Frontiers of Electrical and Electronic Engineering in China , vol. 6, no. 1, pp. 6-16, 2011.
[CrossRef] [SCOPUS Times Cited 63]


[18] L. Breiman, "Bagging predictors" Mach Learn, vol. 24, p. 123-140, 1996.
[CrossRef] [Web of Science Times Cited 9001] [SCOPUS Times Cited 18006]


[19] Y. Freund, R. Schapire, "Experiments with a new boosting algorithm" In Proceedings of thirteenth international conference on machine learning, p. 148-156, 1996.
[CrossRef]


[20] S. Sun, Q. Zhang, "Multiple-view multiple-learner semi-supervised learning" Neural processing letters, vol. 34, no. 3, pp. 229-240., 2011.
[CrossRef] [Web of Science Times Cited 41] [SCOPUS Times Cited 50]


[21] Emtiyaz, Siavash, and MohammadReza Keyvanpour. "Customers behavior modeling by semi-supervised learning in customer relationship management." Advances in information Sciences and Service Sciences(AISS) 3.9 (2012).
[CrossRef] [SCOPUS Times Cited 16]


[22] Hassanzadeh, Hamed, and Mohammadreza Keyvanpour. "A two-phase hybrid of semi-supervised and active learning approach for sequence labeling." Intelligent Data Analysis 17.2 (2013): 251-270.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 9]


[23] Kholghi, Mahnoosh, and MohammadReza Keyvanpour. "Active Learning Framework Combining Semi-Supervised Approach for Data Stream Mining." Intelligent Computing and Information Science. Springer Berlin Heidelberg, 2011. 238-243.
[CrossRef] [SCOPUS Times Cited 3]


[24] Kuncheva, Ludmila I., and Juan J. Rodríguez. "A weighted voting framework for classifiers ensembles." Knowledge and Information Systems 38.2 (2014): 259-275.
[CrossRef] [Web of Science Times Cited 147] [SCOPUS Times Cited 188]


[25] Imani, Maryam Bahojb, Mohamad Reza Keyvanpour, and Reza Azmi. "Semi-supervised Persian font recognition." Procedia Computer Science 3 (2011): 336-342.
[CrossRef] [Web of Science Times Cited 12] [SCOPUS Times Cited 13]


[26] Keyvanpour, Mohammad Reza, and Maryam Bahojb Imani. "Semi-supervised text categorization: Exploiting unlabeled data using ensemble learning algorithms." Intelligent Data Analysis 17.3 (2013): 367-385.
[CrossRef] [Web of Science Times Cited 23] [SCOPUS Times Cited 27]


[27] M. Li , Z. Zhou, " Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples" IEEE Transactions on Systems, Man and Cybernetics, vol. 37, no. 6, p. 1088-1098, 2007.
[CrossRef] [Web of Science Times Cited 247] [SCOPUS Times Cited 331]


[28] L. Didaci, F. Roli, "Using co-training and self-training in semi supervised multiple classifier systems" Lecture Notes in Computer Science, vol. 4109, p. 522-530, 2006.
[CrossRef] [SCOPUS Times Cited 29]


[29] C. Leistner, A. Saffari, J. Santner, H. Bischof, "Semi-supervised random forests" in In Computer Vision 2009 IEEE 12th International Conference on IEEE, 2009.
[CrossRef] [Web of Science Times Cited 76] [SCOPUS Times Cited 111]


[30] Y. Yaslan, Z. Cataltepe, "Co-training with relevant random subspaces" Neurocomputing, vol. 73, no. 10, pp. 1652-1661, 2010.
[CrossRef] [Web of Science Times Cited 64] [SCOPUS Times Cited 76]


[31] J. Wang, S. Luo, X. Zeng, " A random subspace method for co-training" in: Proceedings of the International Joint Conference on Neural Networks (IJCNN 2008), p. 195-200, 2008 .
[CrossRef] [SCOPUS Times Cited 61]


[32] G. Yu, G. Zhang, Z. Yu, C. Domeniconi, J. You, G. Han, "Semi-supervised ensemble classification in subspaces" Applied Soft Computing, vol. 12, no. 5, pp. 1511-1522, 2012.
[CrossRef] [Web of Science Times Cited 29] [SCOPUS Times Cited 38]


[33] K. P. Bennett, A. Demiriz, R. Maclin, "Exploiting unlabeled data in ensemble methods" in Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2002.
[CrossRef] [SCOPUS Times Cited 147]


[34] P. K. Mallapragada, R. Jin, A. K. Jain, Y. Liu, "Semiboost: Boosting for semi-supervised learning" Pattern Analysis and Machine Intelligence, IEEE Transactions, vol. 31, no. 11, pp. 2000-2014., 2009.
[CrossRef] [Web of Science Times Cited 197] [SCOPUS Times Cited 291]


[35] T. Sun,S. Chen, " Locality preserving CCA with applications to data visualization and pose estimation" Image Vision Computing, vol. 25, pp. 531-543, 2007.
[CrossRef] [Web of Science Times Cited 168] [SCOPUS Times Cited 216]


[36] O. Kursun, E. Alpaydin, "Canonical correlation analysis for multi view semi supervised feature extraction" Artificial Intelligence and Soft Computing. Springer Berlin Heidelberg, pp. p. 430-436 2010.
[CrossRef] [SCOPUS Times Cited 15]


[37] A. Kumar, H. Daume, "A co-training approach for multi view spectral clustering" Proceedings of the 28th international conference on machine learning, p. 393-400, 2011.

[38] J. Kittler, M. Hatef, R. P. Duin, J. Matas, " On combining classifiers" IEEE Trans Pattern Anal Mach, 1998. .
[CrossRef] [Web of Science Times Cited 3633] [SCOPUS Times Cited 4587]


[39] Q. Sun, S. Zeng, Y. Liu, P. Heng, D. Xia, "A new method of feature fusion and its application in image recognition" Pattern Recognition, vol. 38, no. 12, p.2437-2448, 2005.
[CrossRef] [Web of Science Times Cited 359] [SCOPUS Times Cited 451]


[40] C. Blake, C. Merz, "UCI Repository of Machine Learning Databases," [].and Computer Science, University of California, Irvine, CA, vol. 55, 1998.

[41] P. Belhumeur, J. Hespanha, D.J. Kriegman, " Eigenfaces vs. fisherfaces : recognition using class specific linear projection" IEEE Trans. Pattern Anal., vol. 19, p. 711-720, 1997.
[CrossRef] [Web of Science Times Cited 7832] [SCOPUS Times Cited 10130]


[42] A. M. Martinez, R. Benavente, "The AR Face Database," CVC Technical Report, vol. 24, 1998.

[43] A. Nielsen, "Multi set canonical correlations analysis and multispectral, truly multi temporal remote sensing data," IEEE Trans. Image Process, vol. 11, 2002.
[CrossRef] [Web of Science Times Cited 226] [SCOPUS Times Cited 251]


[44] A. Sharma, A. Kumar, H. Daume, D. Jacobs, "Generalized multi view analysis: a discriminative latent space", in: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p. 2160-2167, 2012.
[CrossRef] [SCOPUS Times Cited 663]


[45] C. Hou, C. Zhang, Y. Wu, F. Nie, " Multiple view semi-supervised dimensionality reduction" Pattern Recognition , vol. 43, p. 720-730, 2010.
[CrossRef] [Web of Science Times Cited 96] [SCOPUS Times Cited 107]


[46] I. H. Witten and E. Frank, " Data Mining: Practical machine learning tools and techniques" Morgan Kaufmann, 2005.



References Weight

Web of Science® Citations for all references: 22,693 TCR
SCOPUS® Citations for all references: 41,842 TCR

Web of Science® Average Citations per reference: 483 ACR
SCOPUS® Average Citations per reference: 890 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 2024-04-11 21:57 in 231 seconds.




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