Proceedings chapter
Open access

Exploiting Synergistic and Redundant Features for Multimedia Document Classification

Presented at Hamburg (Germany)
Publication date2008

The task of multimedia document classification is challenging due to a diverse set of problems like a high dimensional, sparse and noisy features space, the unknown relevance of the features towards the classification target and the semantic gap between non-informative, low-level features and high-level semantic meanings. As a solution we propose a classification approach combined with feature selection and construction based on feature information interactions. This information-theoretic dependence measure can detect complex feature de pendencies in multi-variate settings. They help to find relevant and non-redundant features and hence allow efficient classification. Experiments on artificial and real world data show the superiority of feature selection based on N-way interactions over greedy, pair-wise dependence measures like correlation and mutual information.

  • Image clustering
  • latent semantic analysis
  • longterm learning
  • relevance feedback
Citation (ISO format)
KLUDAS, Jana, BRUNO, Eric, MARCHAND-MAILLET, Stéphane. Exploiting Synergistic and Redundant Features for Multimedia Document Classification. In: 32nd Annual Conference of the German Classification Society - Advances in Data Analysis, Data Handling and Business Intelligence (GfKl 2008). Hamburg (Germany). [s.l.] : [s.n.], 2008.
Main files (1)
Proceedings chapter (Accepted version)
  • PID : unige:47674

Technical informations

Creation03/06/2015 5:12:06 PM
First validation03/06/2015 5:12:06 PM
Update time03/14/2023 10:58:33 PM
Status update03/14/2023 10:58:33 PM
Last indexation08/29/2023 3:09:01 PM
All rights reserved by Archive ouverte UNIGE and the University of GenevaunigeBlack