Persistence Codebooks for Topological Data Analysis
  Topological data analysis, such as persistent homology has shown beneficial
properties for machine learning in many tasks. Topological representations,
such as the persistence diagram (PD), however, have a complex structure
(multiset of intervals) which makes it difficult to combine with typical
machine learning workflows. We present novel compact fixed-size vectorial
representations of PDs based on clustering and bag of words encodings that cope
well with the inherent sparsity of PDs. Our novel representations outperform
state-of-the-art approaches from topological data analysis and are
computationally more efficient.
