Abstract
In this paper, Multi-View Expectation and Maximization (EM) algorithm for finite mixture models is proposed by us to handle real-world learning problems which have natural feature splits. Multi-View EM does feature split as Co-training and Co-EM, but it considers multi-view learning problems in the EM framework. The proposed algorithm has these impressing advantages comparing with other algorithms in Co-training setting: its convergence is theoretically guaranteed; it can easily deal with more two views learning problems. Experiments on WebKB data demonstrated that Multi-View EM performed satisfactorily well compared with Co-EM, Co-training and standard EM.
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References
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the 11th Annual Conference on Computational Learning Theory, pp. 92–100 (1998)
Muslea, I., Minton, S., Knoblock, C.A.: Active + semi-supervised learning = robust multi-view learning. In: Proceedings of ICML 2002 (2002)
Nigam, K., Ghani, R.: Analyzing the effectiveness and applicability of co-training. In: Proceedings of Information and Knowledge Management, pp. 86–93 (2000)
Bilmes, J.A.: A gentle tutorial of the em algorithm and its application to parameter estimation for gaussian mixture and hidden markov models. Technical report, ICSI Technical Report TR-97-021,UC Berkeley (1997)
Nigam, K., Mccallum, A., Thrun, S., Mitchell, T.: Text classification from labeled and unlabeled documents using em. Machine Learning 39, 103–134 (2000)
Rogati, M., Yang, Y.: High-performing feature selection for text classification. In: Proceedings of the eleventh international conference on Information and knowledge management, McLean, Virginia, USA (2002)
Suen, C.Y.W., Lam, L.: Analyzing the effectiveness and applicability of co-training. In: Proceedings of the First International Workshop on Multiple Classifier Systems (MCS 2000), Cagliari, Italy, pp. 52–66 (2000)
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Yi, X., Xu, Y., Zhang, C. (2005). Multi-view EM Algorithm for Finite Mixture Models. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Data Mining. ICAPR 2005. Lecture Notes in Computer Science, vol 3686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551188_45
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DOI: https://doi.org/10.1007/11551188_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28757-5
Online ISBN: 978-3-540-28758-2
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