  The big data trend has inspired feature-driven learning tasks, which cannot
be handled by conventional machine learning models. Unstructured data produces
very large binary matrices with millions of columns when converted to vector
form. However, such data is often sparse, and hence can be manageable through
the use of sparse random projections.
  This work studies efficient non-iterative and iterative methods suitable for
such data, evaluating the results on two representative machine learning tasks
with millions of samples and features. An efficient Jaccard kernel is
introduced as an alternative to the sparse random projection. Findings indicate
that non-iterative methods can find larger, more accurate models than iterative
methods in different application scenarios.
