Supervised dimensionality reduction seeks to find a low-dimensional feature space that maximizes the separation between classes. We propose a novel supervised dimensionality reduction method, Supervised Quadratic Feature Analysis (SQFA), that exploits the geometry of the data second-order statistics to maximize second-order class separation. Poster.