## 2 Method
### Similarity Filter Algorithm Based on Locally Linear Embedding  
In the SJ-LLE algorithm proposed in this section, ADMM decomposition model pruning optimization is first used to accelerate model training. Combined with the LLE method from Section 2.3 as the loss function during the iterative training process before model pruning, the filter weights before and after pruning are used as the input for the loss function. The optimized model is trained over multiple iterations. Then, the SJ-LLE pruning method is applied to prune the filters of the optimized model. The JS\({}_{-}\) divergence value between every two filters in each network layer is calculated, and the similarity of the filters in each group is evaluated based on the JS\({}_{-}\) divergence value, where a smaller divergence value indicates higher similarity. The similar filter pair to be pruned is determined according to the preset pruning threshold. Finally, the information entropy value of the filters is calculated, and the filter with the smaller information entropy value in the similar filter pair is removed. In this way, the SJ-LLE algorithm reduces the number of model parameters while ensuring the accuracy of the model, without relying on additional storage devices when compressing the model. Consequently, the model can be more widely used in practical scenarios. The flow of the SJ-LLE algorithm is shown in Figure 5.  
The pseudocode of the SJ-LLE method is as follows (Algorithm 3):  
Figure 5: The flow chart of the SJ-LLE method.  
```
Input: dataset \(D\), the number of pre-training iterations \(K^{\prime}\), the number of model weight optimization iterations \(K^{\prime\prime}\), the number of pruning iterations \(K\), N filters to be pruned layer by layer, pruning threshold \(\beta_{i}\), augmented hyperparameter \(\rho\), the weight set \(S_{i}\) of different pruning methods. //Output: Weight parameter \(W\)
1: for \(k\) = 1, 2,..., \(K^{\prime}\) do
2: Sample from \(D\) (\(x,y\))
3: Data normalization and data enhancement preprocessing of (\(x,y\)) yields (\(x^{\prime},y^{\prime}\))
4: Take (\(x^{\prime},y^{\prime}\)) as input to initial training for iterative training
5: end for
6: //Get pre-trained model
7: for \(k\) = 1, 2,..., \(K^{\prime\prime}\) do
8: Take samples from \(D\) (\(x,y\))
9: Take (\(x^{\prime},y^{\prime}\)) as input for model weight optimization training.
10: Apply Algorithm 2 to obtain the pruned weight parameter \(Z\) by using the weight parameter \(W\) of the pre-trained model
11: Take the weight parameters \(W\) and \(Z\) as inputs to Algorithm 1 and solve for \(Z\) in Equation (7).
12: Applying the Adam optimizer to the outer minimum of Equation (11) yields \(\{Z^{i}_{k}\}\)
13: \(U_{i}^{k+1}:=U_{i}^{k}+\rho\Big{(}Z_{i}^{k+1}-W_{i}^{k+1}\Big{)}\)
14: end for
15: //Get the weight optimization model
16: for \(k\) = 1, 2,..., \(K\) do
17: Take samples from \(D\) (\(x,y\))
18: Data normalization and data enhancement preprocessing of (\(x,y\)) yields (\(x^{\prime},y^{\prime}\))
19: Take (\(x^{\prime},y^{\prime}\)) as input for model weight optimization training.
20: Optimize the weight parameter of the model \(W^{\prime}\) using Algorithm 2 to obtain the pruned weight parameter \(Z^{\prime}\)
21: end for
22: //Get weight model after pruning
```  
**Algorithm 3**Similar filter pruning algorithm based on local linear embedding