[Compstats] Seminar Wed @ 2:30pm in *MC5158*: Wayne Oldford

Pascal Poupart ppoupart at cs.uwaterloo.ca
Wed Mar 31 14:10:43 EDT 2010


I'm sorry for the last minute change...  I was just informed that MC6007 
is actually occupied, so the seminar will be in MC5158 instead.

Pascal

Pascal Poupart wrote:
> The room for the seminar will be MC6007.
> Pascal
>
> Pascal Poupart wrote:
>> Hi everyone,
>>
>> Wayne Oldford will give the compstats seminar this Wednesday at 
>> 2:30pm (room TBA).
>> cheers, Pascal
>> ==============================
>>
>> Visualizing High Dimensional Data:
>> Applications of graph theory to statistical graphics
>>
>> Wayne Oldford
>> Department of Statistics & Actuarial Science
>> University of Waterloo
>>
>> Date: Wednesday March 31
>> Time: 2:30pm
>> Location: TBA
>>
>>
>> In statistical data analysis, we are often looking for structure in  
>> high dimensional data.  In classification problems, we are 
>> interested  in how different known classes separate from (and relate 
>> to) one  another in the data space of measured values.  In 
>> clustering, we are  hoping to discover distinct groups of points in 
>> this space.  In model  building, we are often interested in which 
>> data points agree/disagree  with the conjectured model and whether 
>> important structure has been  missed.  And, … we hope to do all of 
>> this without prejudging the  nature of the structure itself, even as 
>> far as to discover the  unanticipated!
>>
>> In three or fewer dimensions, our visual system is an important  
>> asset, as much (even unanticipated) structure can be recognized  
>> effortlessly when points can be plotted so few dimensions.   
>> Unfortunately, even after formal dimension reduction methods have  
>> been applied, we are often faced with many more dimensions than three.
>>
>> In this talk, I will explore some visualization methods for high  
>> dimensional data. I will review and illustrate methods based on  
>> radial, parallel, and orthogonal coordinates.   These three axis  
>> systems have different strengths and weaknesses.  In all cases  
>> however, improvements may be had by casting the axis arrangement in 
>> a  graph theoretic framework.  I will explore the relevant graph  
>> theoretic representations and illustrate their use on real data  
>> sets.  I will pay particular attention to the orthogonal axis system  
>> and show how graph traversal can be used to meaningfully navigate  
>> through high dimensional space.
>>
>> All software used is (or shortly will be) available as a package in  
>> the open source statistical system called R.
>>
>> ________
>>
>> This is based on joint work with Catherine Hurley of the National  
>> University of Ireland, Maynooth and Adrian Waddell of the University  
>> of Waterloo.
>>
>>
>>
>>
>

-- 
------------------------
Pascal Poupart
Associate Professor
David R. Cheriton School of Computer Science
University of Waterloo
200 University Avenue West
Waterloo, Ontario
Canada N2L 3G1
------------------------
Web: http://www.cs.uwaterloo.ca/~ppoupart
Email: ppoupart at cs.uwaterloo.ca 
Telephone: 1-519-888-4567x36239 
Fax: 1-519-885-1208
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