Iterative learning of Fisher linear discriminants for handwritten digit recognition

This paper studies the iterative learning of Fisher linear discriminants (FLDs) for handwritten digit recognition. We present an epoch-limited iterative learning strategy to update the weight vectors and thresholds on condition that the error rates for the current training subsets come down. The within-class scatter matrices being or approximately singular should be moderately reduced in dimensionality but not added with tiny perturbations. We suggest that the thresholds be given by the mean-projected midpoints but not by the least-mean-squared points. Combining the ideas together, this paper proposes a type of integrated FLDs. The experimental results over the MNIST and USPS handwritten digits have demonstrated that the integrated FLDs have obvious advantages over conventional FLDs in the aspects of learning and generalization performances.