  Recently, there has been a surge of interest for quantum computation for its
ability to exponentially speed up algorithms, including machine learning
algorithms. However, Tang suggested that the exponential speed up can also be
done on a classical computer. In this paper, we proposed an algorithm for slow
feature analysis, a machine learning algorithm that extracts the slow-varying
features, with a run time O(polylog(n)poly(d)). To achieve this, we assumed
necessary preprocessing of the input data as well as the existence of a data
structure supporting a particular sampling scheme. The analysis of algorithm
borrowed results from matrix perturbation theory, which was crucial for the
algorithm's correctness. This work demonstrates the possible application and
extent for which quantum-inspired computation can be used.
