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  • $\begingroup$ I'd like some clarification after your edit. You have samples for two users u1 and u2 where some features are hashtag co-occurrence counts? $\endgroup$ Commented Aug 25, 2016 at 2:53
  • $\begingroup$ let's say there are two types of features, topological and sentiment. The topological features are based on structural properties of the pair of users. Adamic adar for example or shortest path. The sentiment features use correlations based on the common hashtags (e.g. size of the rarest common hashtag) $\endgroup$ Commented Aug 25, 2016 at 2:57
  • $\begingroup$ In your second edit, what does from the values that are not gathered around zero due to lack of common hashtags mean? Are you observing that missing hashtag values actually seem to correlate with class labels? If so, that's a vote in favor of my first suggestion, and you should consider adding a binary feature indicating whether hashtag co-occurrence data for the two users is actually missing. $\endgroup$ Commented Aug 25, 2016 at 3:01
  • $\begingroup$ That may be a good suggestion, but that is not what I mean. When visualizing those features you can see some really dense lines around zero (which are all the missing values) but the non-missing values, some times, create nice linearly separable planes which in my mind means that those features have indeed some predictive power. $\endgroup$ Commented Aug 25, 2016 at 3:04
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    $\begingroup$ One problem of imputation by class mean is that when you want to use your model in the future, you won't know the class labels, but you might still have to impute the same feature. $\endgroup$ Commented Oct 24, 2016 at 14:13