Dissimilarity Data in Statistical Model Building and Machine Learning

We explore three papers concerned with two methods for incorporating discrete, noisy, incomplete dissimilarity data into statistical/machine learning models for supervised, semisupervised or unsupervised machine learning. The two methods are RKE (Regularized Kernel Estimation), and RMU (Regularized Manifold Unfolding). Briefly put, the methods use dissimilarity information between objects in a training set to obtain a nonnegative definite matrix of (usually) relatively low rank, which is then used to embed the objects into a (usually) relatively low dimensional Euclidean space, where their coordinates can then be used as attributes in learning models of various types. Some suggestions for further work are noted.