Semi-supervised learning: a brief review

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    Most of the application domain suffers from not having sufficient labeled data whereas unlabeled data is available cheaply. To get labeled instances, it is very difficult because experienced domain experts are required to label the unlabeled data patterns. Semi-supervised learning addresses this problem and act as a half way between supervised and unsupervised learning. This paper addresses few techniques of Semi-supervised learning (SSL) such as self-training, co-training, multi-view learning, TSVMs methods. Traditionally SSL is classified in to Semi-supervised Classification and Semi-supervised Clustering which achieves better accuracy than traditional supervised and unsupervised learning techniques. The paper also addresses the issue of scalability and applications of Semi-supervised learning.


  • Keywords

    Semi-Supervised Learning; Labeled Data; Unlabeled Data; SSL Methods; Training Data; Test Data.

  • References

      [1] A. Jain, M. Murty, and P. Flynn, “Data clusterting: A review acm computing surveys, vol. 31,” 1999.

      [2] O. Chapelle, B. Scholkopf, and A. Zien, “Semi-supervised learning (chapelle, o. et al., eds.; 2006)[book reviews],” IEEE Transactions on Neural Networks, vol. 20, no. 3, pp. 542–542, 2009.

      [3] P. K. Mallapragada, University, Some contributions to semi-supervised learning. Michigan State 2010.

      [4] K. Nigam, A. K. McCallum, S. Thrun, and T. Mitchell, “Text clas-sification from labeled and unlabeled documents using e,” Machine learning, vol. 39, no. 2, pp. 103–134, 2000.

      [5] J. Bernardo, M. Bayarri, J. Berger, A. Dawid, D. Heckerman, A. Smith, and M. West, “Generative or discriminative? Getting the best of both worlds,” Bayesian Stat, vol. 8, no. 3, pp. 3–24, 2007.

      [6] R. K. Ando and T. Zhang, “Two-view feature generation model for semi-supervised learning,” in Proceedings of the 24th international conference on Machine learning. ACM, 2007, pp. 25–32.

      [7] P. Viswanath, K. Rajesh, C. Lavanya, and Y. P. Reddy, “A selective incremental approach for transductive nearest neighbor classification,” in Recent Advances in Intelligent Computational Systems (RAICS), 2011 IEEE. IEEE, 2011, pp. 221–226.

      [8] M. Belkin and P. Niyogi, “Semi-supervised learning on riemannian manifolds,” Machine learning, vol. 56, no. 1-3, pp. 209–239, 2004.

      [9] J. Erman, A. Mahanti, M. Arlitt, I. Cohen, and C. Williamson, “Of-fline/realtime traffic classification using semi-supervised learning,” Per-formance Evaluation, vol. 64, no. 9, pp. 1194–1213, 2007.

      [10] C. Methani, R. Thota, and A. Kale, “Semi-supervised multiple instance learning based domain adaptation for object detection,” in Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing. ACM, 2012, p. 13.

      [11] L. Yao and Z. Ge, “Deep learning of semisupervised process data with hierarchical extreme learning machine and soft sensor application,” IEEE Transactions on Industrial Electronics, vol. 65, no. 2, pp. 1490–1498, 2018.

      [12] D. Chamberlain, R. Kodgule, D. Ganelin, V. Miglani, and R. R. Fletcher, “Application of semi-supervised deep learning to lung sound analysis,” in Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the. IEEE, 2016, pp. 804–807.

      [13] L. Liu, L. Chen, C. P. Chen, Y. Y. Tang et al., “Weighted joint sparse representation for removing mixed noise in image,” IEEE transactions on cybernetics, vol. 47, no. 3, pp. 600–611, 2017.

      [14] B.-H. Chen, J.-L. Yin, and Y. Li, “Image noise removing using semi-supervised learning on big image data,” in Multimedia Big Data (BigMM), 2017 IEEE Third International Conference on. IEEE, 2017, pp. 338–345.

      [15] P. S. S. Aydav and S. Minz, “Modified self-learning with clustering for the classification of remote sensing images,” Procedia Computer Science, vol. 58, pp. 97–104, 2015.

      [16] J. Zhang, Y. Han, J. Tang, Q. Hu, and J. Jiang, “Semi-supervised image-to-video adaptation for video action recognition,” IEEE transactions on cybernetics, vol. 47, no. 4, pp. 960–973, 2017.

      [17] X. Xu, W. Li, D. Xu, and I. W. Tsang, “Co-labeling for multi-view weakly labeled learning,” IEEE transactions on pattern analysis and machine intelligence, vol. 38, no. 6, pp. 1113–1125, 2016.

      [18] H. Grabner, C. Leistner, and H. Bischof, “Semi-supervised on-line boosting for robust tracking,” Computer Vision–ECCV 2008, pp. 234– 247, 2008.

      [19] M.-F. Balcan, A. Blum, P. P. Choi, J. D. Lafferty, B. Pantano, M. R. Rwebangira, and X. Zhu, “Person identification in webcam images: An application of semi-supervised learning,” 2005.

      [20] Y. P. Reddy, P. Viswanath, and B. E. Reddy, “Semi-supervised single-link clustering method,” in Computational Intelligence and Computing Research (ICCIC), 2016 IEEE International Conference on. IEEE, 2016, pp. 1–5.

      [21] T. Joachims, “Transductive inference for text classification using support vector machines,” in ICML, vol. 99, 1999, pp. 200–209.

      [22] S. Basu, M. Bilenko, and R. J. Mooney, “A probabilistic framework for semi-supervised clustering,” in Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ser. KDD ’04. New York, NY, USA: ACM, 2004, pp. 59–68. [Online]. Available.




Article ID: 9977
DOI: 10.14419/ijet.v7i1.8.9977

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