Normalize data before measuring the distance

I am trying to measure the correlation between data (a row vector). I use euclidean distance to measure how correlated each feature to one another. I want to measure the distance of the first feature against the rest of the features. First I normalize my data:

normalizedData = x/sum(X)

second I use Euclidean distance to measure the distance of one feature to the rest of the features

mydata = {}

for Idx = 1:numel(normalizedData)
  euclidean_distance = normalizedData(1) - normailizedData(Idx)
  mydata{1,1} = euclidean_distance;
end

I am wondering if the above way is correct or incorrect? can someone suggest me or give me an opinion.

Thank you

Answer 1

[Shashank Prasanna]

You can use pdist2 to compute pairwise distances:

http://www.mathworks.com/help/stats/pdist2.html

If you want to standardize the data before hand, you can use zscore:

http://www.mathworks.com/help/stats/zscore.html

But if you want to find correlation between to features you use corr

corr(X(:,1),X(:,2))

http://www.mathworks.com/help/stats/corr.html