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SUMMARY:A Maximum Entropy Perspective on Spectral Dimensionality Reduction
  - Prof Neil Lawrence (Sheffield)
DTSTART:20111116T150000Z
DTEND:20111116T160000Z
UID:TALK34586@talks.cam.ac.uk
CONTACT:Zoubin Ghahramani
DESCRIPTION:Spectral approaches to dimensionality reduction typically redu
 ce the dimensionality of a data set through taking the eigenvectors of a L
 aplacian or a similarity matrix. Classical multidimensional scaling also m
 akes use of the eigenvectors of a similarity matrix. In this talk we intro
 duce a maximum entropy approach to designing this similarity matrix. The a
 pproach is closely related to maximum variance unfolding. Other spectral a
 pproaches\, e.g. locally linear embeddings\, turn out to be also closely r
 elated. These  methods can be seen as a sparse Gaussian graphical model wh
 ere correlations between data points (rather than across data features) ar
 e specified in the graph. The hope is that this unifying perspective will 
 allow the relationships between these methods to be better understood and 
 will also provide the groundwork for further research.
LOCATION:Engineering Department\, CBL Room 438
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