We consider a parsimonious version of probabilistic principal component analysis and we proposed an EM algorithm for model inference. The `1 penalty imposed on the principal components makes their interpretation easier, by linking them with only a limited number of original variables. The EM algorithm, easy to implement, is proposed to estimate the model parameters. The slope heuristic is used to select the intensity of the penalty.