Using compressive sensing to build the first layers of a neural network, and more importantly thinking of iterations of different reconstruction solvers as equilvant to layers of neural network, we are getting there.

Image Classification with A Deep Network Model based on Compressive Sensing by Yufei Gan, Tong Zhuo, Chu He

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.

Relevant links:

- Sunday Morning Insight: Faster Than a Blink of an Eye
- Parallel and distributed sparse optimization - implementation -
- k-Sparse Autoencoders
- The STONE Transform: Multi-Resolution Image Enhancement and Real-Time Compressive Video
- Sparse Matrix Factorization: Simple rules for growing neural nets and Provable Bounds for Learning Some Deep Representations

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