Fast Network Pruning and Feature Extraction by using the Unit-OBS Algorithm
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The algorithm described in this article is based on the OBS algorithm by Hassibi, Stork and Wolff ([1] and [2]). The main disadvantage of OBS is its high complexity. OBS needs to calculate the inverse Hessian to delete only one weight (thus needing much time to prune a big net). A better algorithm should use this matrix to remove more than only one weight, because calculating the inverse Hessian takes the most time in the OBS algorithm.
The algorithm, called Unit-OBS, described in this article is a method to overcome this disadvantage. This algorithm only needs to calculate the inverse Hessian once to remove one whole unit thus drastically reducing the time to prune big nets.
A further advantage of Unit-OBS is that it can be used to do a feature extraction on the input data. This can be helpful on the understanding of unknown problems.
[1] Babak Hassibi,et al. Second Order Derivatives for Network Pruning: Optimal Brain Surgeon , 1992, NIPS.
[2] Gregory J. Wolff,et al. Optimal Brain Surgeon and general network pruning , 1993, IEEE International Conference on Neural Networks.
[3] Wolfram Schiffmann,et al. Optimization of the Backpropagation Algorithm for Training Multilayer Perceptrons , 1994 .