How to use different size features in SVM? I want to train a support vector machine with some features. The problem is, one of the features is 1-dimensional (only an angle) and the other is an LBP Histogram, an 58-dimensional vector.

Therefore if I concatenate them, like this (in MATLAB) : 

f = [lbp_histogram; angle];


I got a 59x1 vector, I can train SVM with these vectors, however, in this case the "weight" or "importance" of angle feature is 1/59, whereas lbp has 58/59 weight. What if I want to give them equal weights? Or better, I want to adjust the weights for best performance.
How can I change the weight?

The only 2 options I have in mind is :

Scale the angle feature with the weight like this : 


angle = angle * w_angle
angle = angle ^ w_angle


Do you reccomend one of these, or any other way that I can make angle as important as lbp_histogram?

I hope I stated my problem clearly,
Thanks for any help.
I think you have a misconception.  SVM does not necessarily give the latter 58 features a weight of 58/59.  Rather, SVM learns what weights to use for each feature, based upon what helps it build the best classifier.

So, just use those features and train with them.  Don't try to find a way to manually provide different weights for the features; SVM training automatically takes care of that for you.