[Lisa_seminaires] Time and place change: Oct 16th, 15:30 - Geoffrey Hinton on t-SNE

Pascal Lamblin lamblinp at iro.umontreal.ca
Ven 10 Oct 15:46:40 EDT 2008


Geoffrey Hinton's talk on t-SNE will finally be at 15:30, and
in room AA-6214, because it is now part of DIRO's seminars
<http://www.iro.umontreal.ca/~csuros/ColloquesDIRO/>.

Here are the updated informations:

 Title:   Visualizing high-dimensional data using t-SNE
 Speaker: Geoffrey Hinton
 Date:    Thursday Oct 16th, 2008, 15:30
 Place:   Room 6214 (pav. André-Aisenstadt)

Abstract:
Over the last decade, many new methods have been developed for
visualizing high-dimensional data by giving each data-point a location
in a two-dimensional map. The goal is to represent the separations of
pairs of data-points by the separations of their corresponding
map-points, with an emphasis on representing the small separations
accurately.  I will describe a new method, called t-SNE, that is based
on two ideas. The first idea is to convert the set of pairwise
distances between data-points into a set of probabilities of selecting
pairs of data-points. The selection probability of a pair of points is
proportional to a Gaussian function of their separation.  If the
distances between map-points are converted into pairwise probabilities
in the same way, any given arrangement of map-points can be evaluated
by measuring the divergence between the probability distributions
obtained from the data-points and the map-points.  A good arrangement
of map-points is then found by performing gradient descent in this
divergence.

Unfortunately, if the probabilities of pairs of map-points
are computed using a Gaussian function of their separation, the
difference between the distributions of pairwise distances in
high-dimensional and low-dimensional spaces causes the map-points to
be crowded together in the center of the map. This problem can be
largely overcome by using a heavy-tailed t-distribution when computing
the selection probabilities of pairs of map-points. This leads to maps
that look much better than those produced by other recent methods. In
particular, t-SNE is very good at preserving clusters in the data at
many different scales simultaneously.

The talk describes joint work with Laurens van der Maaten that will
appear in the Journal of Machine Learning Research.

-- 
Pascal


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