Michał Lipiński

Herbert Edelsbrunner, Michał Lipiński, Marian Mrozek et al.

Motivated by questions about simplification of topology, we take a discrete approach to the dependency of simplifying operations, using methods based on combinatorial gradient dynamics. We interpret the filter in persistent homology as a discrete Morse function. This lets us gradually simplify the dynamics in parallel with space and filter, while preserving homology. As a tool, we use shallow pairs, which are simultaneously birth-death pairs and combinatorial vectors. This allows us to extract topological features by the pairing of cells via persistence and simplify them using combinatorially defined cancellations. The main new concept is the depth poset of birth-death pairs, whose minimal elements are shallow pairs and whose linear extensions are sequences of cancellations that reduce the complex to its essential homology. Cancellations of birth-death pairs in a down set of this poset preserve the other birth-death pairs and the poset dependencies between them. An algorithm that constructs the depth poset in two passes of standard matrix reduction is given and proved correct.

Bartosz Zieliński, Michał Lipiński, Mateusz Juda et al.

Persistent homology (PH) is a rigorous mathematical theory that provides a robust descriptor of data in the form of persistence diagrams (PDs). PDs exhibit, however, complex structure and are difficult to integrate in today's machine learning workflows. This paper introduces persistence bag-of-words: a novel and stable vectorized representation of PDs that enables the seamless integration with machine learning. Comprehensive experiments show that the new representation achieves state-of-the-art performance and beyond in much less time than alternative approaches.