Functional Summaries of Persistence Diagrams

TL;DR

This paper reviews and unifies various functional summaries of persistence diagrams in topological data analysis, introduces a generalized framework, and demonstrates their effectiveness in classification and hypothesis testing tasks.

Contribution

It provides a unified framework for functional summaries of persistence diagrams, generalizes persistence landscape functions, and evaluates their performance in statistical applications.

Findings

Unified framework for functional summaries of persistence diagrams.

Generalized persistence landscape functions with theoretical properties.

Effective in classification and two-sample hypothesis testing.

Abstract

One of the primary areas of interest in applied algebraic topology is persistent homology, and, more specifically, the persistence diagram. Persistence diagrams have also become objects of interest in topological data analysis. However, persistence diagrams do not naturally lend themselves to statistical goals, such as inferring certain population characteristics, because their complicated structure makes common algebraic operations--such as addition, division, and multiplication-- challenging (e.g., the mean might not be unique). To bypass these issues, several functional summaries of persistence diagrams have been proposed in the literature (e.g. landscape and silhouette functions). The problem of analyzing a set of persistence diagrams then becomes the problem of analyzing a set of functions, which is a topic that has been studied for decades in statistics. First, we review the various functional summaries in the literature and propose a unified framework for the functional summaries. Then, we generalize the definition of persistence landscape functions, establish several theoretical properties of the persistence functional summaries, and demonstrate and discuss their performance in the context of classification using simulated prostate cancer histology data, and two-sample hypothesis tests comparing human and monkey fibrin images, after developing a simulation study using a new data generator we call the Pickup Sticks Simulator (STIX).