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Keeping things in short. I want to distinguish two classes on a feature space. All what I know are:

  1. the theoretical distributions of the class 0 data on each feature dimension.
  2. the expected distance between the mean of the class 1 data and the mean of the class 0 data on each dimension.

but I do not have any labeled data. I wonder whether LDA can help in this case and how to do it.

Previously, I tried hypothesis testing, but it is very difficult to decide the significance level on each dimension: when I plotted the distribution of accepted data, their sample distributions are far away from the theoretical, often due to the fact that the variance of each class is of a similar scale of the difference between the means of two classes.

PS: all features are correlated.

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    $\begingroup$ What is LDA? Please do not start with acronyms. $\endgroup$
    – ttnphns
    Commented Sep 3, 2013 at 20:45
  • $\begingroup$ LDA is "Linear discriminant analysis". $\endgroup$
    – pitfall
    Commented Sep 3, 2013 at 21:15

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