About
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.
Videos
Invited Speakers

A theory of similarity functions for learning and clustering
Sep 7, 2007
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9025 views

Graph methods and geometry of data
Sep 7, 2007
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9469 views
Contributed Talks

Semidefinite ranking on graphs
Sep 7, 2007
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5095 views

Transductive Rademacher complexities for learning over a graph
Sep 7, 2007
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4508 views

Probabilistic graph partitioning
Sep 7, 2007
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6779 views

Prediction on a graph
Sep 7, 2007
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7584 views

Strings, graphs, invariants
Sep 7, 2007
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4928 views

Frequent graph mining - what is the question?
Sep 7, 2007
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6803 views

Convergence of the graph Laplacian application to dimensionality estimation and ...
Sep 7, 2007
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5093 views

Graph complexity for structure and learning
Sep 7, 2007
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8999 views

On graphical representation of proteins
Sep 7, 2007
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4576 views

Random walk graph kernels and rational kernels
Sep 7, 2007
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8696 views