The Shift-Dimension of Multipersistence Modules
Wojciech Chachólski, René Corbet, Anna-Laura Sattelberger
TL;DR
The paper defines the shift-dimension as a stable, algebraic invariant for multipersistence modules by applying hierarchical stabilization to the zeroth multigraded Betti number $\beta_0$. It develops a comprehensive framework using persistence contours to measure distances between multipersistence modules and proves stability properties, including a linear-time algorithm for computing $\dim_v$ in the bivariate interval-module case. It also extends the concept to multigraded $\mathbb{K}[x_1,\ldots,x_r]$-modules and introduces a versatile class of multivariate contours, linking topological data analysis to combinatorial commutative algebra. The results offer new tools for feature extraction in machine-learning contexts and pave the way for stabilizing higher Betti-number invariants and broader algebraic settings in multiparameter persistence.
Abstract
We present the shift-dimension of multipersistence modules and investigate its algebraic properties. This gives rise to a new invariant of multigraded modules over the multivariate polynomial ring arising from the hierarchical stabilization of the zeroth total multigraded Betti number. We give a fast algorithm for the computation of the shift-dimension of interval modules in the bivariate case. We construct multipersistence contours that are parameterized by multivariate functions and hence provide a large class of feature maps for machine learning tasks.
