Visualizing class structure in data using mutual information

We study linear dimension reducing transforms using maximum mutual information between transformed data and class labels as the criterion to learn the transforms. Renyi quadratic entropy provides a differentiable and computationally feasible criterion on which gradient ascent algorithms can be based without the limitations of methods using only second order statistics, such as PCA or LDA. Application to class structure visualization in exploratory data analysis is presented