  This paper proposes an incremental solution to Fast Subclass Discriminant
Analysis (fastSDA). We present an exact and an approximate linear solution,
along with an approximate kernelized variant. Extensive experiments on eight
image datasets with different incremental batch sizes show the superiority of
the proposed approach in terms of training time and accuracy being equal or
close to fastSDA solution and outperforming other methods.
