A least squares algorithm for a mixture model for compositional data
The estimation of a model for compositional data is studied where the data are approximated by a mixture of latent compositions. This model is variously known as endmember analysis or latent budget analysis. Two estimation procedures are available. The first uses a procedure which is incorrect in the sense that, although it claims to be a least squares procedure, it does not always minimize a least squares criterion. The second uses a maximum likelihood procedure starting from assumptions that are often violated for compositional data. In this paper we propose a constrained (weighted) least squares procedure for the estimation of the model.
mixture model compositional data latent budget model end member model em algorithm weighted least squares algorithm