  To simplify the parameter of the deep learning network, a cascaded
compressive sensing model "CSNet" is implemented for image classification.
Firstly, we use cascaded compressive sensing network to learn feature from the
data. Secondly, CSNet generates the feature by binary hashing and block-wise
histograms. Finally, a linear SVM classifier is used to classify these
features. The experiments on the MNIST dataset indicate that higher
classification accuracy can be obtained by this algorithm.
