Hi Anusaya,
In the context of the CSI Feedback with Autoencoders example in the MATLAB documentation, the target standard deviation value of 0.0212 is used to scale the input data to ensure that the neural network is able to learn effectively from the data while keeping the same order of magnitude as the weights.
The target standard deviation calculation used in this example is based on the following steps:
- Calculate the standard deviation of the input data: This involves computing the standard deviation of the input data matrix (X) along the feature dimension using the 'std' function.
- Calculate the scaling factor: The target standard deviation value is calculated by dividing the desired target standard deviation by the standard deviation of the input data matrix. In the example, the target standard deviation is set to 0.0212, so the scaling factor is calculated as follows:
target_std = 0.0212;
input_std = std(X, [], 1);
scaling_factor = target_std ./ input_std;
3. Scale the input data: The input data matrix can then be scaled by multiplying it by the scaling factor to obtain the scaled input data matrix (X_scaled). In the example, the scaling is performed using the following code:
X_scaled = X .* scaling_factor;
By scaling the input data using this approach, the neural network is expected to be able to learn from the data more effectively, and enable better convergence of the training process.
Hope it helps!
