Learning topology: bridging computational topology and machine learning Davide Moroni1[0000−0002−5175−5126] and M. Antonietta Pascali1[0000−0001−7742−8126] Institute of Information Science and Technologies, National Research Council of Italy, Via Moruzzi, 1 { 56124-Pisa (IT) [email protected] www.isti.cnr.it Abstract. Topology is a classical branch of mathematics, born essen- tially from Euler's studies in the XVII century, which deals with the abstract notion of shape and geometry. Last decades were characterised by a renewed interest in topology and topology-based tools, due to the birth of computational topology and Topological Data Analysis (TDA). A large and novel family of methods and algorithms computing topo- logical features and descriptors (e.g. persistent homology) have proved to be effective tools for the analysis of graphs, 3d objects, 2D images, and even heterogeneous datasets. This survey is intended to be a concise but complete compendium that, offering the essential basic references, allows you to orient yourself among the recent advances in TDA and its applications, with an eye to those related to machine learning and deep learning. Keywords: computational topology · persistent homology · machine learning · deep learning · image and shape analysis · data analysis. 1 Introduction Topology is a branch of mathematics dealing with shape and geometry. Complex- ity and size of current collections of natural or synthetic dataset (2D, 3D, and multidimensional) is rapidly increasing. Hence, the ability to look at the shape of data and to discover patterns in any dimension is gaining great importance. Recently, successful applications of computational topology [27] to data analysis boosted a renewed interest in that field, and Topological Data Analisys (TDA) [10] has earned a prominent place in contemporary research, as a rich family of algorithms and methods from computational topology (e.g. Morse theory or persistent homology) to analyse and visualize data. Focusing on image analysis, in low dimensions (typically 2 or 3), techniques from TDA are used to extract and classify geometric features, e.g. level sets or integral lines in [50]. For what regards Persistent Homology (PH), in [60] the authors describe how to define the Morse complex of a two or three-dimensional grayscale digital image, which is simpler than the cubical complex originally used 2 M.A. Pascali et al. to represent the image and to compute persistent homology. Also, [46] show that persistence diagrams built from functions defined on objects are compact and informative descriptors and, for example, can be used for retrieval of images and shapes. Also, the topological representation of data could provide tools for hierarchical image segmentation, as in [68]. Looking at higher dimensions, e.g. to multidimensional datasets, techniques from TDA have been adapted to develop novel algorithms of data cluster- ing: in 2008 Carlsson, with Singh and Sexton, contributed to founding Ayasdi (www.ayasdi.com), maybe the first machine intelligence platform with a TDA core able to compute groupings and similarity across large and high dimensional data sets, and to generate network maps visually supporting analysts in un- derstanding data clusters (for example showing high dimensional patterns and trends) and which variables are relevant. Several works have shown that TDA can be beneficial in a diverse range of problems, even very distant from each other, such as: studying the manifold of natural image patches [9]; analyzing activity patterns of the visual cortex [61]; in the classification of 3D surface meshes [57, 46]; complex networks [56, 41]; clustering [20, 55]; recognition of 2D object shapes [64]; protein folding [7, 67, 42]; viral evolution [18]. In this survey, we focus specifically on persistent homology (PH), because we found this technique the most interesting with respect to the interplay with machine and deep learning. Outline The following section is devoted to providing the reader basic notions and a historical overview of the main results in the theory of persistence in com- putational topology, starting from early works by P. Frosini, V. Robins and H. Edelsbrunner, which established independently the very first definitions and the- orems. Section 3 deals with the computability of the most used PH descriptors, together with a summary of the software developed to compute PH. Section 4 explains the versatility of such methods, applied in several domains: one of the main factors of the recent interest around PH. Section 5 gives the reader a tour in the most promising applications of TDA in machine and deep learning, showing the great potential of embedding topological tools in the learning pipeline along with implementations of topological layers. These recent efforts have given rise to the novel field of Topological Machine Learning. The last section concludes the paper, and it is devoted to emerging studies focused on the interplay of topo- logical data analysis and deep learning theory: e.g. concerning how to exploit tools from topological analysis to enhance explainability and interpretability of artificial intelligence methods. 2 Persistence homology: history and basic notions Algebraic topology is a branch of mathematics using tools from abstract algebra to study and characterise topological spaces (see [38] for an introductory text- book). The basic aim is to find algebraic invariant able to classify topological Learning topology: bridging computational topology and machine learning 3 spaces up to homeomorphism. Persistent Homology bridges algebraic topology with the Morse theory core idea: exploring topological attributes of an object in an evolutionary context. The concept of persistence was introduced indepen- dently in 1990 by P. Frosini, M. Ferri and collaborators in Bologna (Italy), by V. Robins in 2000 in her PhD thesis devoted to multi-scale topology applied to fractals and dynamics, and by the group of Edelsbrunner at Duke (North Carolina). 2.1 Basics In order to understand the core idea of PH, it is necessary to be familiar with the basics of algebraic topology. A simplicial complex is the standard algebraic object used to represent shapes of any dimension; simplices are its building blocks. Definition 1. A k-simplex is the k-dimensional convex hull of k + 1 vertices. The convex hull of any nonempty subset of the k + 1 vertices is called a face of the simplex A simplicial complex K is a set built from 0-dimensional simplices (0-simplices or points), 1-dimensional simplices (line segments), 2-simplices (triangles), 3- simplices (tetrahedra) and so on. The dimension of K is defined as the largest dimension of any simplex in it. Actually, to be a simplicial complex, K should satisfy the following conditions. Definition 2. A simplicial complex K is a set of simplices such that: { every face of a simplex is also a simplex of K; { the intersection of any two simplices σ1 and σ2 in K is either a face of both σ1 and σ2, or the empty set. Fig. 1. a. An example of a simplex for each dimension from 0 to 3. b. An example of a 3-dimensional simplicial complex. These conditions allow defining the boundary operator, which is fundamental to define the homology, an algebraic object computable (via linear algebra) for K that accounts for the number of connected components, holes, voids, etc. The boundary of a k-simplex @(σj) is the formal sum of the (k − 1)-dimensional faces of σj. For example, the boundary of a triangle fa; b; cg of vertices a; b and c is given by the sum of the edges fb; cg + fa; cg + fa; bg. More formally, k X i @(fv0; : : : ; vkg) = (−1) fv0;:::; v^i; : : : ; vkg i=0 4 M.A. Pascali et al. It's straightforward that a boundary has no boundary: @ ◦ @ = @2 = 0. Let K be a k-simplicial complex, and F a field; in PH the most used F is the two-element field F2 = Z=2Z. Let fσ1; : : : ; σng be the set of p-simplices of K, where p 2 f0; 1; : : : ; kg. Cp(K) denotes the vector space generated over F by the p-dimensional simplices of K; hence, Cp(K) is made of all p-chains, which Pn are the formal sums over the p-simplex c = j=1 ajσj where aj 2 F and σj is a p-simplex in K. Hence, the boundary operator defined above is a linear operator between chain vector spaces: @p : Cp(K) ! Cp−1(K); also, now we define p-cycles Zp(K) and k-boundaries Bp(K) Zp(K) := Ker(@ : Cp ! Cp−1) and Bp(K) := Im(@ : Cp+1 ! Cp): And it yields that: Bp(K) ⊂ Zp(K) ⊂ Cp(K). Finally, the p-th homology group Hp(K) is defined as the quotient space Zp=Bp: two cycles c1 and c2 are homologous if they are in the same homology class: 9 b 2 Bp(K) such that c2 − c1 = b. In algebraic topology, the homology group of a complex is one of the most studied and used. Also, the homology group is linked to the Betti numbers βp, very important topological invariants: βp(K) = dim(Hp(K)). The p-th Betti number, informally, counts the number of p-dimensional holes on a topological surface; for example, a two-dimensional torus has β0 = 1 (it is connected), β1 = 2 (it shows two independent loops on its surface), and β2 = 1 (only one cavity). Back to the case of a simplicial complex K, the 0-dim Betti number is the number of connected components and 2-dim Betti number is the number of voids of K. The homology group and the Betti numbers are able to encode the global topological properties of a shape, represented by a simplicial complex. Persistence needs a core ingredient: filtrations. A filtration of a simplicial complex K is a sequence of nested sub-complexes: ; = K0 ⊂ K1 ⊂ ::: ⊂ Km = K In a few words, think of a filtration as a way to build the given complex iteratively by adding simplices, starting from vertices, step by step.