When Fisher meets Fukunaga-Koontz: A New Look at Linear Discriminants

The Fisher Linear Discriminant (FLD) is commonly used in pattern recognition. It finds a linear subspace that maximally separates class patterns according to Fisher’s Criterion. Several methods of computing the FLD have been proposed in the literature, most of which require the calculation of the so-called scatter matrices. In this paper, we bring a fresh perspective to FLD via the Fukunaga-Koontz Transform (FKT). We do this by decomposing the whole data space into four subspaces, and show where Fisher’s Criterion is maximally satisfied. We prove the relationship between FLD and FKT analytically, and propose a method of computing the most discriminative subspace. This method is based on the QR decomposition, which works even when the scatter matrices are singular, or too large to be formed. Our method is general and may be applied to different pattern recognition problems. We validate our method by experimenting on synthetic and real data.