  In this paper, we propose a speed-up approach for subclass discriminant
analysis and formulate a novel efficient multi-view solution to it. The
speed-up approach is developed based on graph embedding and spectral regression
approaches that involve eigendecomposition of the corresponding Laplacian
matrix and regression to its eigenvectors. We show that by exploiting the
structure of the between-class Laplacian matrix, the eigendecomposition step
can be substituted with a much faster process. Furthermore, we formulate a
novel criterion for multi-view subclass discriminant analysis and show that an
efficient solution for it can be obtained in a similar to the single-view
manner. We evaluate the proposed methods on nine single-view and nine
multi-view datasets and compare them with related existing approaches.
Experimental results show that the proposed solutions achieve competitive
performance, often outperforming the existing methods. At the same time, they
significantly decrease the training time.
