For one, what does it mean for any distribution to be normalized? And two, how do we go about verifying whether a distribution is normalized or not? I understand by computing $$ \frac {X-\text {mean}} {\text {sd}} $$ we get normalized data, but here it's asking to verify whether a distribution is normalized or not.
In the business world, "normalization" typically means that the range of values are "normalized to be from 0.0 to 1.0". "Standardization" typically means that the range of values are "standardized" to measure how many standard deviations the value is from its mean.
We can say that the normalized MSE gives you an idea about the error independently of the absolute mean value. Consider two cases where you have a range of values form 1 to 100 and another from 100 to 100000.
But while I was building my own artificial neural networks, I needed to transform the normalized output back to the original data to get good readable output for the graph.
Is there a way to calculate a normalized wasserstein distance with scipy? EDIT: Let's say I 'm interested in comparing the distances from different individuals that happened to have a different amount of time points in their time series.
The normalized Laplacian is formed from the normalized adjacency matrix: $\hat L = I - \hat A$. $\hat L$ is positive semidefinite. We can show that the largest eigenvalue is bounded by 1 by using the definition of the Laplacian and the Rayleigh quotient.
The normalized squared euclidean distance gives the squared distance between two vectors where there lengths have been scaled to have unit norm. This is helpful when the direction of the vector is meaningful but the magnitude is not.