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ANALYSIS OF STRUCTURE-BASED TECHNIQUES FOR PREPROCESSING THE INDUCED GRAPH OF MASSIVE DATASETS FOR TRANSDUCTIVE LEARNING

The proliferation of the Internet has resulted in a rapid increase in the production of published information. This has led to the emergence of a large class of machine learning problems where the challenge has shifted from scarcity of data to lack of labelled or reliably labelled data. Such tasks are not amenable to inductive learning techniques. In these and many other situations, transductive learning is good alternative. The aim of this project is to compare the propagation of labels in a true underlying graph structure and a hypergraph representation of the data, derived purely from non-graphical attributes. This project focusses on identifying points whose labels are particularly important for classification, a result which could be used in active learning.