06070226

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06070226 Gavriilidis, Vasileios Tefas, Anastasios Exploiting quadratic mutual information for discriminant analysis. Maglogiannis, Ilias (ed.) et al., Artificial intelligence: Theories and applications. 7th Hellenic conference on AI, SETN 2012, Lamia, Greece, May 28--31, 2012. Proceedings. Berlin: Springer (ISBN 978-3-642-30447-7/pbk). Lecture Notes in Computer Science 7297. Lecture Notes in Artificial Intelligence, 90-97 (2012). 2012 Berlin: Springer EN Renyi entropy Parzen estimator feature transform feature extraction mutual information

doi:10.1007/978-3-642-30448-4_12

Summary: Novel criteria that reformulate the Quadratic Mutual Information according to Fisher's Discriminant Analysis are proposed for supervised dimensionality reduction. The proposed method uses a quadratic divergence measure and requires no prior assumptions about class densities. The criteria are optimized using gradient ascent with initialization using random or LDA based projections. Experiments on various datasets are conducted and highlight the superiority of the proposed approach compared to the standard QMI criterion.