[Compstats] Seminar Wed @ 2:30pm in *MC6007*: Wayne Oldford

Pascal Poupart ppoupart at cs.uwaterloo.ca
Tue Mar 30 18:53:08 EDT 2010


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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