title: Vector Space Model: Cosine Similarity vs Euclidean Distance
body: <p>I have corpora of classified text. From these I create vectors. Each vector corresponds to one document. Vector components are word weights in this document computed as TFIDF values. Next I build a model in which every class is presented by a single vector. Model has as many vectors as there classes in the corpora. Component of a model vector is computed as mean of all component values taken from vectors in this class. For unclassified vectors I determine similarity with a model vector by computing cosine between these vectors.</p>
  
  <p>Questions: </p>
  
  <p>1) Can I use Euclidean Distance between unclassified and model vector to compute their similarity? </p>
  
  <p>2) Why Euclidean distance can not be used as similarity measure instead of cosine of angle between two vectors and vice versa?</p>
  
  <p>Thanks!</p>