Structure Preserving Embedding Blake Shaw, Tony Jebara ICML 2009 (Best Student Paper nominee) Presented by Feng Chen.

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Presentation transcript:

Structure Preserving Embedding Blake Shaw, Tony Jebara ICML 2009 (Best Student Paper nominee) Presented by Feng Chen

Outline Motivation Solution Experiments Conclusion

Motivation Graphs exist everywhere: web link networks, social networks, molecules networks, k-nearest neighbor graph, and more. Graph Embedding – Embed a graph in a low dimensional space. – What used for? Visualization, Compression, and More. – Existing methods: Planar Embedding (no crossing arcs); Spectral Embedding (preserve local distances); Laplacian Eigenmaps (preserve local distances).

Problem Statement Given only the connectivity of a graph, can we efficiently recover low-dimensional point coordinates for each node such that these points can be easily used to reconstruct the original structure of the network?

Optimization Formulation

Justifications Why can preserve the local distances?

Justifications Interpret the slack variable 

Implementation It is about finding a positive semi-definite matrix that can maximize an objective function, subject to some constraints about the matrix. This category of optimization problem is called “Semi-definite Programming” Available MATLAB tools include CSDP and SDP- LR.

Related Work Spectral Embedding – Find the d leading eigen-vectors of the adjacency matrix A as the coordinates. Laplacian Eigenmaps – Employ a spectral decomposition of the graph Laplacian L=D-A, where D=diag(A1), and use the d eigenvectors of L with the smallest nonzero eigenvalues. – Look weird? But this approach has the fundamental from manifold theories in the field of topology!!

Experiments

Experiment

Conclusion Innovative idea! Technically simple!

Thank You! Any Question?