Optimal transform coding of Gaussian mixtures for joint classification/reconstruction

In a variety of applications, classification systems operate on compressed signals. The design of optimal transform coders optimizing a joint classification/reconstruction criterion was explored, where classification accuracy is measured using the Chernoff bound on probability of misclassification and reconstruction quality is measured using mean-squared error (MSE) distortion. Under a high-rate assumption, local optimality properties of the Karhunen-Loeve transform (KLT) for a certain class of Gaussian mixtures under a joint classification/MSE measure was shown. Analytical expressions for optimal bit-allocation were derived. This generalizes classical optimality properties of the KLT for Gaussian sources under the MSE criterion.