  We illustrate the advantages of distance weighted discrimination for
classification and feature extraction in a High Dimension Low Sample Size
(HDLSS) situation. The HDLSS context is a gender classification problem of face
images in which the dimension of the data is several orders of magnitude larger
than the sample size. We compare distance weighted discrimination with Fisher's
linear discriminant, support vector machines, and principal component analysis
by exploring their classification interpretation through insightful
visuanimations and by examining the classifiers' discriminant errors. This
analysis enables us to make new contributions to the understanding of the
drivers of human discrimination between males and females.
