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.
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Uploaded videos:
Invited Speakers
Graph methods and geometry of data
Sep 07, 2007
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9467 Views
A theory of similarity functions for learning and clustering
Sep 07, 2007
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9019 Views
Contributed Talks
Convergence of the graph Laplacian application to dimensionality estimation and ...
Sep 07, 2007
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5088 Views
Probabilistic graph partitioning
Sep 07, 2007
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6774 Views
Prediction on a graph
Sep 07, 2007
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7583 Views
Frequent graph mining - what is the question?
Sep 07, 2007
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6800 Views
Transductive Rademacher complexities for learning over a graph
Sep 07, 2007
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4506 Views
Strings, graphs, invariants
Sep 07, 2007
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4923 Views
On graphical representation of proteins
Sep 07, 2007
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4570 Views
Graph complexity for structure and learning
Sep 07, 2007
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8994 Views
Semidefinite ranking on graphs
Sep 07, 2007
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5088 Views
Random walk graph kernels and rational kernels
Sep 07, 2007
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8687 Views