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_angleangle = angle ^ w_angleDo 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.
How to use different size features in SVM?
machine learning;linear algebra;matrices;cluster