Mixture model estimation with soft labels

Etienne Côme (1) , Latifa Oukhellou (1) , Thierry Denoeux (2) , Patrice Aknin (1)

1 INRETS/LTN - Laboratoire des Technologies Nouvelles
2 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]

This paper addresses classification problems in which the class membership of training data is only partially known. Each learning sample is assumed to consist in a feature vector and an imprecise and/or uncertain ``soft'' label $m_i$ defined as a Dempster-Shafer basic belief assignment over the set of classes. This framework thus generalizes many kinds of learning problems including supervised, unsupervised and semi-supervised learning. Here, it is assumed that the feature vectors are generated from a mixture model. Using the General Bayesian Theorem, we derive a criterion generalizing the likelihood function. A variant of the EM algorithm dedicated to the optimization of this criterion is proposed, allowing us to compute estimates of model parameters. Experimental results demonstrate the ability of this approach to exploit partial information about class labels.

Domaines: Statistiques [math.ST]; Théorie [stat.TH]; Machine Learning [stat.ML]

Etienne Côme, Latifa Oukhellou, Thierry Denoeux, Patrice Aknin. Mixture model estimation with soft labels. Fourth International Workshop on Soft Methods in Probabilities and Statistics, Sep 2008, Toulouse, France. pp.165-174. ⟨hal-00446806⟩