EM Algorithm for Partially Known Labels
Mixture models are widely used for clustering or discrimination problems. Estimating the parameters of such models can be viewed as an incomplete data problem and has thus often been handled by the Expectation-Maximization (EM) algorithm. It has been shown that this method can integrate additional information such as the label of some observations. In this paper we propose a generalization of this approach which can take into account partial information about the observation labels. An example illustrates the relevance of the proposed method for mixture density estimation.
KeywordsMixture Model Additional Knowledge Label Indicator Finite Mixture Distribution Component Gaussian Mixture
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