  Transformer is a powerful tool for many natural language tasks which is based
on self-attention, a mechanism that encodes the dependence of other tokens on
each specific token, but the computation of self-attention is a bottleneck due
to its quadratic time complexity. There are various approaches to reduce the
time complexity and approximation of matrix is one such. In Nystr\"omformer,
the authors used Nystr\"om based method for approximation of softmax. The
Nystr\"om method generates a fast approximation to any large-scale symmetric
positive semidefinite (SPSD) matrix using only a few columns of the SPSD
matrix. However, since the Nystr\"om approximation is low-rank when the
spectrum of the SPSD matrix decays slowly, the Nystr\"om approximation is of
low accuracy. Here an alternative method is proposed for approximation which
has a much stronger error bound than the Nystr\"om method. The time complexity
of this same as Nystr\"omformer which is $O\left({n}\right)$.
