We present a novel method to construct low-dimensional linear discriminative subspaces in this paper. Our method is
simple and the calculation cost is little. The new subspace we construct is low dimensional while retaining discriminative
information of original feature space. This means that we can make full use of discriminative information both in
original ranks space and original null space by constructing a low-dimensional subspace and its discriminative matrix.
The performance achieved by our method shows its great potential in resolving image classification problems.