Transductively Learning from Positive Examples Only
Kristiaan Pelckmans, Johan A. K. Suykens · 2009
Abstract. This paper considers the task of learning a binary labeling of the vertices of a graph, given only a small set of positive examples and knowledge of the desired amount of positives. A learning machine is described maximizing the precision of the prediction, a combinatorial optimization problem which can be rephrased as a S-T mincut problem. For validation, we consider the movie recommendation dataset of MOVIELENS 1. For each user we have given a collection of (ratings of) movies which are liked well, and the task is to recommend a disjoint set of movies which are most probably of interest to the user. 1