Abstract
Topology is a classical branch of mathematics, born essentially 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 topological 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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adams, H., et al.: Persistence images: a stable vector representation of persistent homology. J. Mach. Learn. Res. 18(1), 218–252 (2017)
Adams, H., Tausz, A., Vejdemo-Johansson, M.: javaPlex: a research software package for persistent (co)homology. In: Hong, H., Yap, C. (eds.) ICMS 2014. LNCS, vol. 8592, pp. 129–136. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44199-2_23
Barsocchi, P., Cassará, P., Giorgi, D., Moroni, D., Pascali, M.: Computational topology to monitor human occupancy. In: Multidisciplinary Digital Publishing Institute Proceedings, vol. 2, p. 99 (2018)
Bauer, U., Kerber, M., Reininghaus, J.: Dipha (a distributed persistent homology algorithm). Software available at (2014). https://github.com/DIPHA/dipha
Bauer, U., Kerber, M., Reininghaus, J., Wagner, H.: Phat-persistent homology algorithms toolbox. J. Symbol. Comput. 78, 76–90 (2017)
Biasotti, S., Cerri, A., Frosini, P., Giorgi, D., Landi, C.: Multidimensional size functions for shape comparison. J. Math. Imag. Vis. 32(2) 161 (2008)
Bowman, G., et al.: Structural insight into RNA hairpin folding intermediates. J. Am. Chem. Soc. 130(30), 9676–9678 (2008)
Bubenik, P.: Statistical topological data analysis using persistence landscapes. J. Mach. Learn. Res. 16(3), 77–102 (2015)
Carlsson, G., Ishkhanov, T., de Silva, V., Zomorodian, A.: On the local behavior of spaces of natural images. Int. J. Comput. Vision 76, 1–12 (2008)
Carlsson, G.: Topology and data. Bull. Amer. Math. Soc. 46, 255–308 (2009)
Carlsson, G., De Silva, V.: Zigzag persistence. Found. Comput. Math. 10(4), 367–405 (2010)
Carlsson, G., Gabrielsson, R.B.: Topological approaches to deep learning. In: Baas, N.A., Carlsson, G.E., Quick, G., Szymik, M., Thaule, M. (eds.) Topological Data Analysis, pp. 119–146. Springer International Publishing, Cham (2020)
Carrière, M., Cuturi, M., Oudot, S.: Sliced wasserstein kernel for persistence diagrams. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70. p. 664–673. ICML 2017, JMLR.org (2017)
Carriére, M., Oudot, S.Y., Ovsjanikov, M.: Stable topological signatures for points on 3d shapes. Comput. Graph. Forum 34(5), 1–12 (2015)
Carrière, M., Chazal, F., Ike, Y., Lacombe, T., Royer, M., Umeda, Y.: Perslay: A neural network layer for persistence diagrams and new graph topological signatures (2020)
Cerri, A., Fabio, B., Ferri, M., Frosini, P., Landi, C.: Betti numbers in multidimensional persistent homology are stable functions. Math. Methods Appl. Sci. 36, 1543–1557 (2013)
Cerri, A., Ferri, M., Giorgi, D.: Retrieval of trademark images by means of size functions. Graph. Models 68(5), 451–471 (2006)
Chan, J.M., Carlsson, G., Rabadan, R.: Topology of viral evolution. Proc. Natl. Acad. Sci. 110(46), 18566–18571 (2013)
Chazal, F., Fasy, B.T., Lecci, F., Rinaldo, A., Wasserman, L.: Stochastic convergence of persistence landscapes and silhouettes. In: Proceedings of the Thirtieth Annual Symposium on Computational Geometry, p. 474–483. SOCG 2014, Association for Computing Machinery, New York, USA (2014)
Chazal, F., Guibas, L.J., Oudot, S.Y., Skraba, P.: Persistence-based clustering in riemannian manifolds. J. ACM, 60(6) (2013)
Cohen-Steiner, D., Edelsbrunner, H., Harer, J.: Stability of persistence diagrams. Discr. Comput. Geom. 37, 103–120 (2007)
Cohen-Steiner, D., Edelsbrunner, H., Morozov, D.: Vines and vineyards by updating persistence in linear time. In: Proceedings of the Twenty-second Annual Symposium On Computational Geometry, pp. 119–126 (2006)
De Silva, V., Morozov, D., Vejdemo-Johansson, M.: Dualities in persistent (co) homology. Inverse Prob. 27(12), 124003 (2011)
Dey, T.K., Shi, D., Wang, Y.: SimBa: an efficient tool for approximating Rips-filtration persistence via simplicial batch collapse. J. Exp. Algorithmics 24(1), 1–6 (2019)
Di Fabio, B., Ferri, M.: Comparing persistence diagrams through complex vectors. In: Murino, V., Puppo, E. (eds.) ICIAP 2015. LNCS, vol. 9279, pp. 294–305. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23231-7_27
Dindin, M., Umeda, Y., Chazal, F.: Topological data analysis for arrhythmia detection through modular neural networks. In: Goutte, C., Zhu, X. (eds.) Canadian AI 2020. LNCS (LNAI), vol. 12109, pp. 177–188. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-47358-7_17
Edelsbrunner, H., Harer, J.: Computational Topology: An Introduction. American Mathematical Society (2010)
Edelsbrunner, H., Letscher, D., Zomorodian, A.: Topological persistence and simplification. Discr. Comput. Geom. 28, 511–533 (2002)
Fasy, B.T., Kim, J., Lecci, F., Maria, C.: Introduction to the r package tda. arXiv preprint arXiv:1411.1830 (2014)
Frosini, P.: A distance for similarity classes of submanifolds of a euclidean space. Bull. Australian Math. Soc. 42(3), 407–416 (1990)
Frosini, P.: Discrete computation of size functions. J. Comb. Inf. Syst. Sci. 17(3–4), 232–250 (1992)
Frosini, P.: Measuring shapes by size functions. In: Casasent, D.P. (ed.) Intelligent Robots and Computer Vision X: Algorithms and Techniques, vol. 1607, pp. 122–133. International Society for Optics and Photonics, SPIE (1992)
Gabrielsson, R.B., Nelson, B.J., Dwaraknath, A., Skraba, P.: A topology layer for machine learning. In: Chiappa, S., Calandra, R. (eds.) Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 108, pp. 1553–1563. PMLR (2020)
Gamble, J., Heo, G.: Exploring uses of persistent homology for statistical analysis of landmark-based shape data. J. Multi. Anal. 101(9), 2184–2199 (2010)
Ghrist, R.: Barcodes: the persistent topology of data. Bull. Am. Math. Soc. 45(1), 61–75 (2008)
Gidea, M., Katz, Y.: Topological data analysis of financial time series: landscapes of crashes. Phys. A 491, 820–834 (2018)
Guss, W.H., Salakhutdinov, R.: On characterizing the capacity of neural networks using algebraic topology. CoRR abs/1802.04443 (2018)
Hatcher, A.: Algebraic topology. Cambridge University Press, Cambridge (2000)
Hofer, C., Kwitt, R., Niethammer, M., Uhl, A.: Deep learning with topological signatures. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, p. 1633–1643. NIPS 2017, Curran Associates Inc., Red Hook, NY, USA (2017)
Hofer, C.D., Kwitt, R., Niethammer, M.: Learning representations of persistence barcodes. J. Mach. Learn. Res. 20(126), 1–45 (2019). http://jmlr.org/papers/v20/18-358.html
Horak, D., Maletić, S., Rajković, M.: Persistent homology of complex networks. J. Stat. Mech: Theory Exp. 2009(03), P03034 (2009)
Ichinomiya, T., Obayashi, I., Hiraoka, Y.: Protein-folding analysis using features obtained by persistent homology. Biophys. J. 118(12), 2926–2937 (2020)
Kim, K., Kim, J., Zaheer, M., Kim, J.S., Chazal, F., Wasserman, L.: Pllay: Efficient topological layer based on persistence landscapes (2020)
Kusano, G., Fukumizu, K., Hiraoka, Y.: Persistence weighted gaussian kernel for topological data analysis. In: Proceedings of the 33rd International Conference on International Conference on Machine Learning. vol. 48, pp. 2004–2013. ICML 2016, JMLR.org (2016)
Le, T., Yamada, M.: Persistence fisher kernel: A riemannian manifold kernel for persistence diagrams. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems, pp. 10028–10039. NIPS 2018, Curran Associates Inc., Red Hook, NY, USA (2018)
Li, C., Ovsjanikov, M., Chazal, F.: Persistence-based structural recognition. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2003–2010 (2014)
Maria, C., Boissonnat, J.-D., Glisse, M., Yvinec, M.: The Gudhi Library: Simplicial Complexes and Persistent Homology. In: Hong, H., Yap, C. (eds.) ICMS 2014. LNCS, vol. 8592, pp. 167–174. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44199-2_28
Masood, T.B., et al.: An overview of the topology toolkit. In: TopoInVis 2019-Topological Methods in Data Analysis and Visualization (2019)
Mischaikow, K., Nanda, V.: Morse theory for filtrations and efficient computation of persistent homology. Discr. Comput. Geom. 50(2), 330–353 (2013)
Monasse, P., Guichard, F.: Fast computation of a contrast-invariant image representation. IEEE Trans. Image Process. 9(5), 860–872 (2000)
Morozov, D.: Dionysus, a c++ library for computing persistent homology. https://www.mrzv.org/software/dionysus/ (2007)
Naitzat, G., Zhitnikov, A., Lim, L.H.: Topology of deep neural networks. J. Mach. Learn. Res. 21(184), 1–40 (2020)
Nanda, V.: Perseus: the persistent homology software. Software available at http://www.sas.upenn.edu/vnanda/perseus (2012)
Otter, N., Porter, M.A., Tillmann, U., Grindrod, P., Harrington, H.A.: A roadmap for the computation of persistent homology. EPJ Data Sci. 6(1), 17 (2017)
Pereira, C.M., de Mello, R.F.: Persistent homology for time series and spatial data clustering. Expert Syst. Appl. 42(15), 6026–6038 (2015)
Petri, G., Scolamiero, M., Donato, I., Vaccarino, F.: Topological strata of weighted complex networks. PLoS ONE 8(6), 1–8 (2013)
Reininghaus, J., Huber, S., Bauer, U., Kwitt, R.: A stable multi-scale kernel for topological machine learning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4741–4748 (2015)
Rieck, B., et al.: Neural persistence: a complexity measure for deep neural networks using algebraic topology. In: International Conference on Learning Representations (ICLR) (2019)
Robins, V., Wood, P., Sheppard, A.: Theory and algorithms for constructing discrete morse complexes from grayscale digital images. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1646–1658 (2011)
Singh, G., Memoli, F., Ishkhanov, T., Sapiro, G., Carlsson, G., Ringach, D.: Topological analysis of population activity in visual cortex. J. Vis. 8(8), 1–18 (2008)
Skraba, P., Ovsjanikov, M., Chazal, F., Guibas, L.: Persistence-based segmentation of deformable shapes. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, pp. 45–52 (2010)
Som, A., Choi, H., Ramamurthy, K.N., Buman, M.P., Turaga, P.: Pi-net: a deep learning approach to extract topological persistence images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2020)
Turner, K., Mukherjee, S., Boyer, D.: Persistent homology transform for modeling shapes and surfaces. Inf. Inf. A J. IMA 3(4), 310–344 (2014)
Umeda, Y.: Time series classification via topological data analysis. Trans. Japanese Soc. Artif. Intell. 32(3), D-G72\(_{1}\)- -12 (2017)
Verri, A., Uras, C., Frosini, P., Ferri, M.: On the use of size functions for shape analysis. Biol. Cybern. 70, 99–107 (1993)
Xia, K., Wei, G.: Persistent homology analysis of protein structure, flexibility, and folding. Int. J. Numer. Method Biomed. Eng. 30(8), 814–844 (2014)
Xu, Y., Carlinet, E., Géraud, T., Najman, L.: Hierarchical segmentation using tree-based shape spaces. IEEE Trans. Pattern Anal. Mach. Intell. 39(3), 457–469 (2017)
Zomorodian, A., Carlsson, G.: Computing persistent homology. Discr. Comput. Geom. 3, 249–274 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Moroni, D., Pascali, M.A. (2021). Learning Topology: Bridging Computational Topology and Machine Learning. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12665. Springer, Cham. https://doi.org/10.1007/978-3-030-68821-9_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-68821-9_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-68820-2
Online ISBN: 978-3-030-68821-9
eBook Packages: Computer ScienceComputer Science (R0)