CoMeT | <%=title %>
Collaborative Management of Talks Hello! sign in or register
Bookmark Talks, Share with Friends, and We Recommend More!
Advanced Search
Home
Calendar
Series
Speaker
Groups
Connections
 
 
 
 
Talk Detail
Posted: peterb on  Aug 26 11:41:00 AM
Title: An information retrieval approach to visualization of high-dimensional data  
Speaker:
Jaakko Peltonen
Associate Professor, School of Information Sciences, University of Tampere
Host: Peter Brusilovsky
Sponsor: University of Pittsburgh  >  School of Information Sciences
Series: iSchool Colloquium Series
  Big Data Colloquium Series
Date: Sep 02, 2015 1:30 PM - 2:30 PM
URL: http://ischool.pitt.edu/documents/flyers/2015-09-Peltonen-flyer.pdf
Location: IS 522, School of Information Sciences, 135 N. Bellefield Ave.
Slide: http://users.ics.aalto.fi/~jtpelto/pittsburgh2015_jaakkopeltonen_2.pdf
Groups Posted: Big Data Information Retrieval Intelligent Systems Program PAWS Group 
Bookmarked by:  dsteinberg Yun Huang Peijun Ren peterb chirayu Roya Hosseini EvgenyKarataev
Detail:

Abstract:

Nonlinear dimensionality reduction methods are often used to visualize high-dimensional data, but many proposed methods have been designed for other related tasks such as manifold learning. It has been difficult to assess the quality of visualizations since the task has not been well-defined. We give a rigorous definition for a specific visualization task, resulting in quantifiable goodness measures and new visualization methods. The task is information retrieval given the visualization: to find similar data based on the similarities shown on the display. The fundamental tradeoff between precision and recall of information retrieval can then be quantified in visualizations as well. The resulting family of visualization methods, called NeRV (Neighbor Retrieval Visualizer), performs very well in unsupervised visualization tasks, and has been extended in many ways to supervised visualization, parametric visualization, fast visualization scalable to big data, and interactive visualization, and has been incorporated as part of exploratory information seeking systems.

Bio:

Jaakko Peltonen is an Associate Professor of statistics (data analysis) at the School of Information Sciences, University of Tampere, Finland; he is also currently an academy research fellow at Aalto University, Finland, where he is a PI of the Probabilistic Machine Learning research group. He is an associate editor of Neural Processing Letters and an editorial board member of Heliyon. He has served in organizing committees of seven international conferences and one international summer school, has served in program committees of 28 international conferences/workshops and has performed referee duties for numerous international journals and conferences. He has 74 publications and has 730 citations so far (h-index 14). He is an expert in statistical machine learning methods for exploratory data analysis, visualization of data, and learning from multiple sources.

Interest Area: Computer & Information Science & Engineering, Mathematical & Physical Sciences
 
 
People Who Bookmarked This Talk, Also Bookmarked
 
 
Export
RSS Feed: RSS 2.0
ATOM Feed: Atom
iCalendar: iCal
Share: Bookmark and Share
 
Google Calendar:
 
 
 
 
CoMeT Blog
©2009-2017 CoMeT - Supported by Google Grant
School of Information Sciences, University of Pittsburgh, 135 North Bellefield Avenue, Pittsburgh, PA 15260
Real Time Web Analytics