What does "zero-meaned vector" mean I'm trying to reproduce an algorithm designed in a paper. And everything is going well except one thing:

It says we considered the lengths zero-meaned accelerometer vectors and created a feature for the mean and standard deviation of this value. and I do not understand what is it zero-meaned vectors?

Example dataset:


-0.6946377  12.680544  0.50395286
5.012288    11.264028  0.95342433
4.903325    10.882658  -0.08172209
-0.61291564 18.496431  3.0237172
-1.1849703  12.108489  7.205164
1.3756552   -2.4925237 -6.510526
-0.61291564 10.56939   5.706926
-0.50395286 13.947236  7.0553403


Can any body help me?

I found only this information https://www.quora.com/What-does-it-mean-when-a-vector-is-zero-mean but I'm not sure about it.

Thank you.
"Zero-meaned" means the vector has been transformed so that its mean is 0.

Typically, you would do this by subtracting the mean of each column from that column. (This is for dimensional as well as algorithmic reasons; you don't want to subtract a person's weight from their height.)

It sounds like here they're actually talking about the row mean--that is, $(-0.6946377, 12.680544,  0.50395286)$ would be transformed to $(-4.857924, 8.5172577, -3.65933344, 4.1632863, 7.40047)$, where the first three are the original features minus the row mean, the fourth is the row mean, and the fifth is the standard deviation of the original features.

This would make sense if the three have the same units (if they're all accelerations at the same scale, this works), and so you want a separate measure of how much it's being accelerated at all and how much it's being accelerated in a particular direction.