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I Which reduced vector should be used in cosine similarity?

  1. Oct 27, 2016 #1
    In Latent semantic analysis, the truncated singular value decomposition (SVD) of a term-document matrix ##A_{mn}## is
    $$A=U_rS_rV^T_r$$
    In many references including wikipedia, the new reduced document column vector in r-space is scaled by the singular value ##S## before comparing it with other vectors by cosine similarity. This yields ##q^T_r S## where ##q^T_r## is just the component of a column vector of the ##V^T_r## matrix. and ##S## is the corresponding singular value.
    But in other references, only ##q^T_r## is used for cosine similarity. which one of them is more appropriate and why?
     
  2. jcsd
  3. Nov 1, 2016 #2
    Thanks for the thread! This is an automated courtesy bump. Sorry you aren't generating responses at the moment. Do you have any further information, come to any new conclusions or is it possible to reword the post? The more details the better.
     
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