Multiple classifier learning is integrated into sparse dictionary learning.

The proposed algorithm simultaneously updates dictionary and classifiers.

The proposed method can largely improve the discriminability of sparse codes.

An interesting insight into label consistency from the view of ensemble learning.

The experiments show the excellent performance of the proposed method.