# Feature selection on n different values

I have a .csv file with data in the following form:

moment_1;moment_2;moment_3;force_x;force_y;force_z;...
-0,02131267;-1,6032766088;5,9906811787;5,40010285;0,0203;86,44227467;...
2599;-1,70091039344;-1,3044809;-0,0406673590;-2,60896180797;43,2334;...


The file is very large and I need to put it in an interactive visualization, that's why I need to reduce the data points without changing the overall structure too much.

Many data points are very close to each other as seen in the following image:

My approach was to define a threshold and filter all points which have a distance to the previous point lower than the threshold. But I think that's not an optimal solution because, when I remove one index, I need to remove it from the other data array too, otherwise the structure is changed.

Are there better approaches?

• What are you using to process the data? Are you trying to reduce noise in the signal as well? Aug 21 '17 at 15:24
• Currently, I just display the data in a visualization. Yes, I'm trying to figure out how I can smooth the data. Aug 22 '17 at 9:49
• Have a search on dsp.stackexchange.com It has more questions on smoothing / subsampling. Is this from a gyro / accelerometer? Aug 22 '17 at 11:36