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Exploiting Synergistic and Redundant Features for Multimedia Document Classification

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Published in 32nd Annual Conference of the German Classification Society - Advances in Data Analysis, Data Handling and Business Intelligence (GfKl 2008). Hamburg (Germany). 2008
Abstract 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.
Keywords Image clusteringlatent semantic analysislongterm learningrelevance feedback
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Research groups Computer Vision and Multimedia Laboratory
Viper group
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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. https://archive-ouverte.unige.ch/unige:47674

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Deposited on : 2015-03-06

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