Exploiting document feature interactions for efficient information fusion in high dimensional spaces

Abstract

Information fusion, especially for high dimensional multimedia data, is still an open research problem. In this article, we present a new approach to target this problem. Feature information interaction is an information-theoretic dependence measure that can determine synergy and redundancy between attributes, which then can be exploited with feature selection and construction towards more efficient information fusion. This also leads to improved performances for algorithms that rely on information fusion like multimedia document classification. We show that synergetic and redundant feature pairs require different fusion strategies for optimal exploitation. The approach is compared to classical feature selection strategies based on correlation and mutual information.