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University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Progress on the connection between spectral embedding and network models used by the probability, statistics and machine-learning communities
Progress on the connection between spectral embedding and network models used by the probability, statistics and machine-learning communitiesAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact INI IT. STS - Statistical scalability In this talk, I give theoretical and methodological results, based on work spanning Johns Hopkins, the Heilbronn Institute for Mathematical Research, Imperial and Bristol, regarding the connection between various graph spectral methods and commonly used network models which are popular in the probability, statistics and machine-learning communities. An attractive feature of the results is that they lead to very simple take-home messages for network data analysis: a) when using spectral embedding, consider eigenvectors from both ends of the spectrum; b) when implementing spectral clustering, use Gaussian mixture models, not k-means; c) when interpreting spectral embedding, think of “mixtures of behaviour” rather than “distance”. Results are illustrated with cyber-security applications. This talk is part of the Isaac Newton Institute Seminar Series series. This talk is included in these lists:This talk is not included in any other list Note that ex-directory lists are not shown. |
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