Persistent Homology and Machine Learning
Abstract
In this position paper, we present a brief overview of the ways topological tools, in particular persistent homology, has been applied to machine learning and data analysis problems. We provide an introduction to the area, including an explanation as to how topology may capture higher order information. We also provide numerous references for the interested reader and conclude with some current directions of research.
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