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 data1 demonstrated that Multi-View EM performed satisfactorily well compared with Co-EM, Co-training and standard EM. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Yi, X., Xu, Y., & Zhang, C. (2005). Multi-view EM algorithm for finite mixture models. In Lecture Notes in Computer Science (Vol. 3686, pp. 420–425). Springer Verlag. https://doi.org/10.1007/11551188_45
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