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SUMMARY:Spectral Clustering - Mr Roger Grosse (MIT)\; Alex Matthews
DTSTART:20121122T143000Z
DTEND:20121122T160000Z
UID:TALK41691@talks.cam.ac.uk
CONTACT:Colorado Reed
DESCRIPTION:The term "Spectral Clustering" refers to a family of clusterin
 g algorithms related by the fact they exploit methods from spectral graph 
 theory. These methods have proved popular in the machine learning communit
 y and are relatively straightforward to implement. This talk will be prima
 rily a tutorial on basic results of spectral graph theory combined with an
  overview of various ways these techniques can be used for clustering. We 
 will cover the basic properties of graph Laplacians\, the relationship bet
 ween the first eigenvector and the normalized cut\, and some simple techni
 ques to obtain clusterings from the eigenvectors of the Laplacian. We'll t
 hen discuss the Nystrom method\, an approximate method which is faster and
  can generalize to points outside the original graph. Since this is a tuto
 rial\, no prior reading will be required.
LOCATION:Engineering Department\, CBL Room 438
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