After reducing the dimensionality of a dataset, I am getting negative feature values

I used a Dimensionality Reduction method (discussion here: Random projection algorithm pseudo code) on a large dataset.

After reducing the dimension from 1000 to 50, I get my new dataset where each sample looks like:

[ 1751. -360. -2069. ..., 2694. -3295. -1764.]

Now I am a bit confused, because I don't know what negative feature values supposed to mean. Is it okay to have negative features like this? Because before the reduction, each sample was like this:

3, 18, 18, 18, 126开发者_如何学编程 ...

Is it normal or am I doing something wrong?

I guess you implemented the algorithm from this paper.

As the projection matrix has some negative entries it is ok that the projection maps positve to negative values. So the change in the signs does not indicate an error.