Successive Subspace Learning: An Overview
This is an incremental overview paper summarizing an existing method for researchers in unsupervised learning.
The paper provides an overview of Successive Subspace Learning (SSL), a lightweight unsupervised feature learning method that leverages statistical properties of data units, noting its promising results on small datasets.
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.