2 citations · 3 across the 3 of their papers we have counts for
5 papers
Persistent reachability homology in machine learning applications
Luigi Caputi, Nicholas Meadows, Henri Riihimäki
We explore the recently introduced persistent reachability homology (PRH) of digraph data, i.e. data in the form of directed graphs. In particular, we study the effectiveness of PR…
Metrics for Learning in Topological Persistence
Henri Riihimäki, José Licón-Saláiz
Persistent homology analysis provides means to capture the connectivity structure of data sets in various dimensions. On the mathematical level, by defining a metric between the ob…
A topological data analysis based classification method for multiple measurements
Henri Riihimäki, Wojciech Chachólski, Jakob Theorell +2
Machine learning models for repeated measurements are limited. Using topological data analysis (TDA), we present a classifier for repeated measurements which samples from the data…
Metrics and stabilization in one parameter persistence
Wojciech Chachólski, Henri Riihimäki
We propose a new way of thinking about one parameter persistence. We believe topological persistence is fundamentally not about decomposition theorems but a central role is played…
Generalized persistence analysis based on stable rank invariant
Henri Riihimäki, Wojciech Chacholski
We believe three ingredients are needed for further progress in persistence and its use: invariants not relying on decomposition theorems to go beyond 1-dimension, outcomes suitabl…