Combining persistent homology and invariance groups for shape comparison

In many applications concerning the comparison of data expressed by R^m-valued functions defined on a topological space X, the invariance with respect to a given group G of self-homeomorphisms of X is required. While persistent homology is quite efficient in the topological and qualitative comparison of this kind of data when the invariance group G is the group Homeo(X) of all self- homeomorphisms of X, this theory is not tailored to manage the case in which G is a proper subgroup of Homeo(X), and its invariance appears too general for several tasks. This paper proposes a way to adapt persistent homology in order to get invariance just with respect to a given group of self-homeomorphisms of X. The main idea consists in a dual approach, based on considering the set of all G-invariant non-expanding operators defined on the space of the admissible filtering functions on X. Some theoretical results concerning this approach are proven and two experiments are presented. An experiment illustrates the application of the proposed technique to compare 1D-signals, when the invariance is expressed by the group of affinities, the group of orientation-preserving affinities, the group of isometries, the group of translations and the identity group. Another experiment shows how our technique can be used for image comparison

On the construction of group invariant non expansive operators

In questa tesi vengono illustrati metodi di topologia computazionale negli ambiti di topological data analysis e shape comparison. Nello specifico, dato uno spazio topologico X e un sottogruppo G di Homeo(X), viene studiato un set di dati Φ composto da funzioni definite su X, continue e limitate, a valori reali. Per farlo si utilizzano degli operatori G-invarianti non espansivi (GINO), che si sono dimostrati efficaci per approssimare la pseudo-distanza naturale. In particolare in questo lavoro vengono studiati metodi per la costruzione di tali operatori, sfruttando le proprietà algebriche delle variabili del problema. È importante osservare che il gruppo G viene sempre considerato come variabile, in quanto un cambio dell’osservatore può generalmente coincidere con un cambio dell’invarianza a cui si è interessati

Persistent topology for natural data analysis - A survey

Natural data offer a hard challenge to data analysis. One set of tools is being developed by several teams to face this difficult task: Persistent topology. After a brief introduction to this theory, some applications to the analysis and classification of cells, lesions, music pieces, gait, oil and gas reservoirs, cyclones, galaxies, bones, brain connections, languages, handwritten and gestured letters are shown

Vectorizing Distributed Homology with Deep Set of Set Networks

Distributed homology, a topological invariant, holds potential as an instrument for uncov- ering insights into the structural characteristics of complex data. By considering both the density and connectivity of topological spaces, it offers the potential for a more detailed and stable understanding of the underlying structure of data sets. This is particularly beneficial when confronting noisy, real-world data. Despite its potential, the complexity and unstructured nature of distributed homology pose hurdles for practical use. This thesis tackles these issues by proposing a novel pipeline that fuses distributed homology and supervised learning techniques. The goal is to facilitate the effective incorporation of distributed homology into a wide array of supervised learning tasks. Our approach is anchored on the DeepSet network, an architecture adept at managing set inputs. Using this, we devise a comprehensive framework specifically designed to handle inputs composed of a set of sets. Furthermore, we present a dedicated architecture for distributed homology, designed to boost robustness to noise and overall performance. This approach shows marked improvements over full persistent homology methods for both synthetic and real data. While our results may not yet rival state-of-the-art performance on real data, they demonstrate the potential for distributed invariants to enhance the efficiency of topolog- ical approaches. This indicates a promising avenue for future research and development, contributing to the refinement of topological data analysis.Masteroppgave i informatikkINF399MAMN-PROGMAMN-IN

Towards a topological-geometrical theory of group equivariant non-expansive operators for data analysis and machine learning

The aim of this paper is to provide a general mathematical framework for group equivariance in the machine learning context. The framework builds on a synergy between persistent homology and the theory of group actions. We define group-equivariant non-expansive operators (GENEOs), which are maps between function spaces associated with groups of transformations. We study the topological and metric properties of the space of GENEOs to evaluate their approximating power and set the basis for general strategies to initialise and compose operators. We begin by defining suitable pseudo-metrics for the function spaces, the equivariance groups, and the set of non-expansive operators. Basing on these pseudo-metrics, we prove that the space of GENEOs is compact and convex, under the assumption that the function spaces are compact and convex. These results provide fundamental guarantees in a machine learning perspective. We show examples on the MNIST and fashion-MNIST datasets. By considering isometry-equivariant non-expansive operators, we describe a simple strategy to select and sample operators, and show how the selected and sampled operators can be used to perform both classical metric learning and an effective initialisation of the kernels of a convolutional neural network.Comment: Added references. Extended Section 7. Added 3 figures. Corrected typos. 42 pages, 7 figure