Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis

Multi-Instance Mixture Models and Semi-Supervised Learning

Multi-instance (MI) learning is a variant of supervised learning where labeled examples consist of bags (i.e. multi-sets) of feature vectors instead of just a single feature vector. Under standard assumptions, MI learning can be understood as a type of semisupervised learning (SSL). The difference between MI learning and SSL is that positive bag labels provide weak label information for the ins...

Instance Selection in Semi-supervised Learning

Semi-supervised learning methods utilize abundant unlabeled data to help to learn a better classifier when the number of labeled instances is very small. A common method is to select and label unlabeled instances that the current classifier has high classification confidence to enlarge the labeled training set and then to update the classifier, which is widely used in two paradigms of semi-supe...

Semi-Supervised Learning Literature Survey

Multi-Manifold Semi-Supervised Learning

We study semi-supervised learning when the data consists of multiple intersecting manifolds. We give a finite sample analysis to quantify the potential gain of using unlabeled data in this multi-manifold setting. We then propose a semi-supervised learning algorithm that separates different manifolds into decision sets, and performs supervised learning within each set. Our algorithm involves a n...

A Survey on Inductive Semi-supervised Learning