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Contribution Details

Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title Hebbian principal component clustering for information retrieval on a crowdsourcing platform
Organization Unit
  • Thomas Niederberger
  • Norbert Stoop
  • Markus Christen
  • Thomas Ott
Presentation Type paper
Item Subtype Original Work
Refereed No
Status Published in final form
  • English
ISBN 978-3-8007-3444-3
Page Range 1 - 4
Event Title Nonlinear Dynamics of Electronic Systems
Event Type conference
Event Location Wolfenbüttel, Germany
Event Start Date July 11 - 2012
Event End Date July 13 - 2012
Publisher IEEE
Abstract Text Crowdsourcing, a distributed process that involves outsourcing tasks to a network of people, is increasingly used by companies for generating solutions to problems of various kinds. In this way, thousands of people contribute a large amount of text data that needs to already be structured during the process of idea generation in order to avoid repetitions and to maximize the solution space. This is a hard information retrieval problem as the texts are very short and have little predefined structure. We present a solution that involves three steps: text data preprocessing, clustering, and visualization. In this contribution, we focus on clustering and visualization by presenting a Hebbian network approach that is able to learn the principal components of the data while the data set is continuously growing in size. We compare our approach to standard clustering applications and demonstrate its superiority with respect to classification reliability on a real-world example.
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