Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Feature extraction by non parametric mutual information maximization
The Journal of Machine Learning Research
Linear projection method based on information theoretic learning
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Statistical Properties of Error Estimators in Performance Assessment of Recognition Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
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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.