Unsupervised Identification of Points of Interest for Semi-Supervised Learning

We propose using a data preprocessing technique to automate the selection and labeling of points of interest from unlabeled data for the purpose of semisupervised clustering. The preprocessing approach, called MembershipMap, strives to extract the underlying sub-concepts of each attribute, and uses the orthogonal union of these sub-concepts to define a new, semantically richer, space. Using the MembershipMap, points of interest could be identified and labeled in a completely unsupervised way. We show that these points of interest could be used to convert an unsupervised learning problem into a simpler semi-supervised problem. Thus, benefiting from the advantage of semi-supervised learning without the need to manually identify and label a subset of control points. The proposed approach is applied to the problem of image segmentation