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Exploiting Quadratic Mutual Information for Discriminant Analysis
Vasileios Gavriilidis and Anastasios Tefas
Aristotle University of Thessaloniki, Department of Informatics, Thessaloniki, Greece[email protected]
[email protected]
Abstract. 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.
Keywords: Renyi Entropy, Parzen estimator, Feature transform, Feature extraction, Mutual information
LNAI 7297, p. 90 ff.
[email protected]
© Springer-Verlag Berlin Heidelberg 2012