This paper explores a large margin approach to learning a linear transform for
dimensionality reduction. The method assumes a trained Gaussian mixture model for
the each class to be discriminated and trains a linear transform with respect to
the model using stochastic gradient descent. Results are presented showing
improvements in state classification for individual frames and reduced word error
rate in a large vocabulary speech recognition problem after maximum likelihood
training and boosted maximum mutual information training.