  Sparse coding can learn good robust representation to noise and model more
higher-order representation for image classification. However, the inference
algorithm is computationally expensive even though the supervised signals are
used to learn compact and discriminative dictionaries in sparse coding
techniques. Luckily, a simplified neural network module (SNNM) has been
proposed to directly learn the discriminative dictionaries for avoiding the
expensive inference. But the SNNM module ignores the sparse representations.
Therefore, we propose a sparse SNNM module by adding the mixed-norm
regularization (l1/l2 norm). The sparse SNNM modules are further stacked to
build a sparse deep stacking network (S-DSN). In the experiments, we evaluate
S-DSN with four databases, including Extended YaleB, AR, 15 scene and
Caltech101. Experimental results show that our model outperforms related
classification methods with only a linear classifier. It is worth noting that
we reach 98.8% recognition accuracy on 15 scene.
