Abstract:

We introduce a new method for discovering latent topics in sets of
objects, such as documents. Our method, which we call PARIS (for Principal
Atoms Recognition In Sets), aims to detect principal sets of elements,
representing latent topics in the data, that tend to appear frequently
together. These latent topics, which we refer to as ''atoms'', are used as the
basis for clustering, classification, collaborative filtering, and more. 
We develop a target function which balances compression and low error of
representation, and the algorithm which minimizes the function. 
Optimization of the target function enables an automatic discovery of the 
number of atoms, representing the dimensionality of the data, and the atoms
themselves, all in a single iterative procedure. We demonstrate PARIS's
ability to discover latent topics, even when those are arranged
hierarchically, on synthetic, documents and movie ranking data, showing
improved performance compared to existing algorithms, such as LDA, on 
text analysis and collaborative filtering tasks.

Short Bio:
Michal Aharon received her B.Sc. (summa cum laude), M.Sc. (cum laude), 
and PhD degrees in Computer Science from the Technion, Israel Institute of
Technology, Haifa, Israel, in 2001, 2004, and 2007, respectively, working
under the supervision of Prof. Ron Kimmel and Prof. Michael Elad. Both the
M.Sc and Ph.D. dissertations are in the field of image and signal
processing, concentrating on dimensionality reduction and sparse
representation of signals. Michal has been working at HP-laboratories in
Israel since November 2006. She concentrates both on color science and on
data analysis applications.


Refreshments served from 14:15 on,
 	Lecture starts at 14:30