The workshop focuses on the fundamentals of graph theory relevant to learning, with emphasis on the applications of spectral clustering, visualisation and transductive learning.

Methods from graph theory have made an impact in Machine Learning recently through two avenues. The first arises when we view the data samples as the vertices of the graph with the similarity between the examples encoded by the weights on the edges. This view of the data can be used to motivate a number of techniques, including spectral clustering, nonlinear dimensionality reduction, visualisation, transductive and semi-supervised classification.

The second reason for involving graph theory is through the representation of complex objects by graphs. This could be for objects that have a natural graph structure such as molecules or gene networks, or for cases where a feature extraction phase constructs a graph, as for example in natural language processing or computer vision. A key development in this area has been the realisation that feature spaces involving exponentially many features can be used implicitly via kernels that compute in polynomial time inner products between projections into the feature space. This use of graph representations is becoming common in many applications of machine learning making a focus on this topic relevant to a number of application areas, particularly bioinformatics and natural language processing.

For more information visit the Workshop website.