Estimating Tree-Structured Covariance Matrices via Mixed-Integer Programming
; Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, PMLR 5:41-48, 2009.
We present a novel method for estimating tree-structured covariance matrices directly from observed continuous data. A representation of these classes of matrices as linear combinations of rank-one matrices indicating object partitions is used to formulate estimation as instances of well-studied numerical optimization problems. In particular, our estimates are based on projection, where the covariance estimate is the nearest tree-structured covariance matrix to an observed sample covariance matrix. The problem is posed as a linear or quadratic mixed-integer program (MIP) where a setting of the integer variables in the MIP specifies a set of tree topologies of the structured covariance matrix. We solve these problems to optimality using efficient and robust existing MIP solvers. We present a case study in phylogenetic analysis of expression in yeast gene families and a comparison using simulated data to distance-based tree estimating procedures.