Applying the projection W T x (where x is a training instance) would give us m new features, however, for "both computational and statistical reasons"<cite> (Blitzer et al., 2006</cite>; Ando and Zhang, 2005 ) a low-dimensional approximation of the original feature space is computed by applying Singular Value Decomposition (SVD) on W (step 4).