Use correlation matrix scores as starting weights and bias inputs for neural network? I have a neural net that's generating an average 15% error across the three outputs it gives. My problem is two of the three really have about a 2% error while the third has around 40%.

I was wondering if anyone has used results from a correlation matrix on the raw data to set starting parameters for what features are valued more relative to the labels. If this is possible, or logical, how would you initialize this for two input features if your model's first layer has say eight nodes - or at least more than two? I'm using Keras btw, but even a theoretical explanation would be helpful. 

If it doesn't make sense to follow this path, does anyone have recommendations at correctly the imbalance?
The way I see it is that NN can learn nonlinear features, so I wonder: does your correlation matrix on the raw data take into account nonlinear connections between features to see the full picture? Probably, it doesn't. As the topic is not about the single-layer nn (which would be an example of linear regression), the final connections (values of each weight) will be created by the contribution of many layers. Therefor it seems to me, that setting start weights on the proposed way wouldn't be high relevant.  

Speaking about imbalancing, I'd recommend to experiment with loss functions. You can set higher penalty for mistakes on a certain class.