1 citations · 1 across the 1 of their papers we have counts for
4 papers
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…