2 citations · 3 across the 3 of their papers we have counts for
Showing 2019Show all
3 papers · 1 filter
cs.LG2019★ 1 cited
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…
cs.LG2019
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…
math.AT2019
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…