Lab Home | Phone | Search
Center for Nonlinear Studies  Center for Nonlinear Studies
 Colloquia Archive 
 Postdoc Seminars Archive 
 Quantum Lunch 
 Quantum Lunch Archive 
 CMS Colloquia 
 Q-Mat Seminars 
 Q-Mat Seminars Archive 
 P/T Colloquia 
 Kac Lectures 
 Kac Fellows 
 Dist. Quant. Lecture 
 Ulam Scholar 
 CNLS Fellowship Application 
 Student Program 
 Past Visitors 
 History of CNLS 
 Maps, Directions 
 CNLS Office 
Wednesday, March 21, 2012
3:00 PM - 4:00 PM
CNLS Conference Room (TA-3, Bldg 1690)


Eyes Wide Open: Iterative Discovery in Large Data Sets without Premature Specialization

Kiri Wagstaff
Jet Propulsion Laboratory

What is the best way to dive in and explore a new data set? We pose a new machine learning problem, iterative discovery, that seeks to enable users to interactively explore a large data set and quickly identify items of interest. At each iteration, the system selects an item for the user to review, and the user provides feedback as to whether it is interesting or not. The system must retain a strong exploratory bias to avoid premature specialization (seeking items similar to those known to be interesting, and missing out on different but equally interesting items). Unlike active learning, the goal is to select items of most use to the user, not to the system. I will describe a solution called Discovery through Eigenbasis Modeling of Uninteresting Data (DEMUD) that avoids premature specialization on the positive (interesting) class by modeling the negative (uninteresting) feedback only. DEMUD is especially effective when the class of interest is rare and/or he terogeneous. I will share results of experiments with image, planetary science, and astronomy data, in which we find that DEMUD discovers items of interest faster than methods that model the positive class, including active learning.

Host: Garrett Kenyon,, 7-1900, IS & T