Discussion: Latent variable graphical model selection via convex optimization

DISCUSSION: LATENT VARIABLE GRAPHICAL MODEL SELECTION VIA CONVEX OPTIMIZATION By Emmanuel J. Candés and Mahdi Soltanolkotabi Stanford University We wish to congratulate the authors for their innovative contribution, which is bound to inspire much further research. We find latent variable model selection to be a fantastic application of matrix decomposition methods, namely, the superposition of low-rank and sparse elements. Clearly, the methodology introduced in this paper is of potential interest across many disciplines. In the following, we will first discuss this paper in more detail and then reflect on the versatility of the low-rank sparse decomposition. The proposed scheme is an extension of the graphical lasso of Yuan and Lin [15] (see also [1, 6]), which is a popular approach for learning the structure in an undirected Gaussian graphical model.