  Successive Subspace Learning (SSL) offers a light-weight unsupervised feature
learning method based on inherent statistical properties of data units (e.g.
image pixels and points in point cloud sets). It has shown promising results,
especially on small datasets. In this paper, we intuitively explain this
method, provide an overview of its development, and point out some open
questions and challenges for future research.
