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ICAPR
2005
Springer

Multi-view EM Algorithm for Finite Mixture Models

10 years 4 months ago
Multi-view EM Algorithm for Finite Mixture Models
In this paper, Multi-View Expectation and Maximization algorithm for finite mixture models is proposed by us to handle realworld learning problems which have natural feature splits. Multi-View EM does feature split as Co-training and Co-EM, but it considers multiview learning problems in the EM framework. The proposed algorithm has these impressing advantages comparing with other algorithms in Cotraining setting: it can be applied for both unsupervised learning and semi-supervised learning tasks; it can easily deal with more two views learning problems; it can simultaneously utilize different classifiers and different optimization criteria such as ML and MAP in different views for learning; its convergence is theoretically guaranteed. Experiments on synthetic data, USPS data and WebKB data1 demonstrated that Multi-View EM performed satisfactorily well compared with Co-EM, Cotraining and standard EM.
Xing Yi, Yunpeng Xu, Changshui Zhang
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where ICAPR
Authors Xing Yi, Yunpeng Xu, Changshui Zhang
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