Ghost image processing for minimum covariance determinants

In this paper we describe a ghost image processing application to the problem of finding the minimum covariance determinant (MCD) estimator of multi-variate shape and location. The MCD is resistant to contamination and has other desirable statistical properties but is difficult to compute. Ghost image processing offers an opportunity to make use of knowledge of the form of solutions when constructing algorithms to solve hard combinatorial optimization problems. Experimental results and comparisons with steepest descent lend additional insights.