This turned out to be a lot simpler than I originally thought. The answer/process is as follows:
Given a set of input vectors such as the following:
[1,0,1,0], [0,1,0,1]
The data is already constrained between 0 and 1 to minimize the variance. However, in the case of my data I have something more like the following:
[0,145,0,132],[0,176,0,140]
This causes the variance in some input features to be much larger and you would therefore not be able to use the weight vector as an indicator of feature importance. Therefore, in order for the weight vector to be an indicator of importance we much normalize the data first by dividing by the feature max.
For the above set that would be: [0,176,0,140]
This would result in a set of uniform feature vectors and would also result in the weight vector being an indicator of feature importance.