  High-dimensional data requires scalable algorithms. We propose and analyze
three scalable and related algorithms for semi-supervised discriminant analysis
(SDA). These methods are based on Krylov subspace methods which exploit the
data sparsity and the shift-invariance of Krylov subspaces. In addition, the
problem definition was improved by adding centralization to the semi-supervised
setting. The proposed methods are evaluated on a industry-scale data set from a
pharmaceutical company to predict compound activity on target proteins. The
results show that SDA achieves good predictive performance and our methods only
require a few seconds, significantly improving computation time on previous
state of the art.
