title: Support Vector Machine on Highly Sparse Dataset
body: <p>I am currently with problem of using <strong>SVM on highly sparse dataset</strong> . The problem is I have N*M dataset where N-number of example and M- number of features . <strong>Each N has hardly 10 features present</strong> . </p>
  
  <p>As of now I am representing the features as binary vectors ie 1 if the feature is present in given 0 if now . <strong>The dataset is balance and I am getting accuracy of 94% .Also ROC is 0.93</strong> .  I am trying to understand why this accuracy is coming high . </p>
  
  <p><strong>(1)Could anyone please guide me to related paper where sparse dataset is giving high accuracy where I can try to find the reason of such high accuracy .</strong></p>
  
  <p><strong>(2) Also I am planning to use SciKit Learn . Could anyone suggest me which function I should use in scikit learn in SVM which is designed for such high sparsicity dataset .</strong></p>
  
  <p><strong>(3)Also if any one can try to explain me the reason behind such high accuracy , then it would be great .</strong></p>
  
  <p><strong>(4) Also if I change binary feature representation to some weighted representation , will it give me some advantage .</strong></p>