H. Adams, T. Emerson, M. Kirby, R. Neville, C. Peterson et al., Persistence Images: A Stable Vector Representation of Persistent Homology, Journal Machine Learning Research, vol.18, issue.8, pp.1-35, 2017.

U. Bauer and M. Lesnick, Induced Matchings of Barcodes and the Algebraic Stability of Persistence, Annual Symposium on Computational Geometry, SOCG'14, pp.162-191, 2015.
DOI : 10.1145/2582112.2582168

C. Berg, J. Christensen, and P. Ressel, Harmonic Analysis on Semigroups: Theory of Positive Definite and Related Functions, 1984.
DOI : 10.1007/978-1-4612-1128-0

P. Bubenik, Statistical Topological Data Analysis using Persistence Landscapes, Journal Machine Learning Research, vol.16, pp.77-102, 2015.

G. Carlsson, Topology and data, Bulletin of the American Mathematical Society, vol.46, issue.2, pp.255-308, 2009.
DOI : 10.1090/S0273-0979-09-01249-X

M. Carrì-ere, S. Oudot, and M. Ovsjanikov, Stable Topological Signatures for Points on 3D Shapes, Proceedings 13th Symposium Geometry Processing, 2015.

M. Carrì-ere, M. Cuturi, and S. Oudot, Sliced Wasserstein Kernel for Persistence Diagrams, 1706.

C. Chang and C. Lin, LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, pp.27-28, 2011.
DOI : 10.1145/1961189.1961199

F. Chazal, D. Cohen-steiner, M. Glisse, L. Guibas, and S. Oudot, Proximity of persistence modules and their diagrams, Proceedings of the 25th annual symposium on Computational geometry, SCG '09, pp.237-246, 2009.
DOI : 10.1145/1542362.1542407
URL : https://hal.archives-ouvertes.fr/inria-00292566

F. Chazal, D. Cohen-steiner, L. Guibas, F. Mémoli, and S. Oudot, Gromov-Hausdorff Stable Signatures for Shapes using Persistence, Computer Graphics Forum, vol.33, issue.5, pp.1393-1403, 2009.
DOI : 10.1109/TPAMI.2006.208
URL : https://hal.archives-ouvertes.fr/hal-00772413

F. Chazal, V. De-silva, M. Glisse, and S. Oudot, The structure and stability of persistence modules, 2016.
DOI : 10.1007/978-3-319-42545-0
URL : https://hal.archives-ouvertes.fr/hal-01330678

X. Chen, A. Golovinskiy, and T. Funkhouser, A Benchmark for 3D Mesh Segmentation, ACM Trans. Graph, vol.2873, issue.3, pp.1-7312, 2009.
DOI : 10.1145/1576246.1531379

D. Cohen-steiner, H. Edelsbrunner, and J. Harer, Stability of Persistence Diagrams, Discrete & Computational Geometry, vol.37, issue.1, pp.103-120, 2007.
DOI : 10.1007/s00454-006-1276-5

H. Edelsbrunner and J. Harer, Computational Topology: an introduction, 2010.
DOI : 10.1090/mbk/069

H. Edelsbrunner and J. Harer, Persistent homology???a survey, Contemporary mathematics, vol.453, pp.257-282, 2008.
DOI : 10.1090/conm/453/08802

B. Fabio, . Di, and M. Ferri, Comparing Persistence Diagrams Through Complex Vectors, 2015.
DOI : 10.1007/978-3-319-23231-7_27

Z. Guo, L. Zhang, and D. Zhang, A completed modeling of local binary pattern operator for texture classification, IEEE Trans. Image Processing, pp.1657-1663, 2010.

J. Hertzsch, R. Sturman, and S. Wiggins, DNA Microarrays: Design Principles for Maximizing Ergodic, Chaotic Mixing, Small, pp.202-218, 2007.
DOI : 10.1007/978-1-4757-3896-4

Y. Hiraoka, T. Nakamura, A. Hirata, E. Escolar, K. Matsue et al., Hierarchical structures of amorphous solids characterized by persistent homology, Proceedings National Academy of Science, 2016.
DOI : 10.1006/jcph.1995.1039
URL : http://www.pnas.org/content/113/26/7035.full.pdf

G. Kusano, K. Fukumizu, and Y. Hiraoka, Persistence Weighted Gaussian Kernel for Topological Data Analysis, Proceedings 33rd International Conference on Machine Learning, 2004.

G. Kusano, K. Fukumizu, and Y. Hiraoka, Kernel method for persistence diagrams via kernel embedding and weight factor, 1706.

R. Kwitt, . Huber, . Stefan, . Niethammer, . Marc et al., Statistical Topological Data Analysis -A Kernel Perspective, Advances in Neural Information Processing Systems 28, pp.3070-3078, 2015.

C. Li, M. Ovsjanikov, and F. Chazal, Persistence-Based Structural Recognition, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2003.
DOI : 10.1109/CVPR.2014.257
URL : https://hal.archives-ouvertes.fr/hal-01073075

V. Morariu, B. Srinivasan, V. Raykar, R. Duraiswami, D. et al., Automatic online tuning for fast Gaussian summation, Advances Neural Information Processing Systems 21, pp.1113-1120, 2009.

T. Ojala, T. Mäenpää, M. Pietikäinen, J. Viertola, J. Kyllönen et al., Outex - new framework for empirical evaluation of texture analysis algorithms, Object recognition supported by user interaction for service robots, pp.701-706, 2002.
DOI : 10.1109/ICPR.2002.1044854

J. Rabin, G. Peyré, J. Delon, and M. Bernot, Wasserstein Barycenter and Its Application to Texture Mixing, International Conference Scale Space Variational Methods Computer Vision, pp.435-446, 2011.
DOI : 10.1109/18.119725

A. Rahimi and B. Recht, Random Features for Large-Scale Kernel Machines, Advances Neural Information Processing Systems, pp.1177-1184, 2008.

J. Reininghaus, S. Huber, U. Bauer, and R. Kwitt, A stable multi-scale kernel for topological machine learning, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1412.
DOI : 10.1109/CVPR.2015.7299106
URL : http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Reininghaus_A_Stable_Multi-Scale_2015_CVPR_paper.pdf

J. Reininghaus, S. Huber, U. Bauer, and R. Kwitt, A stable multi-scale kernel for topological machine learning, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2015.7299106
URL : http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Reininghaus_A_Stable_Multi-Scale_2015_CVPR_paper.pdf

V. Robins and K. Turner, Principal component analysis of persistent homology rank functions with case studies of spatial point patterns, sphere packing and colloids, Physica D: Nonlinear Phenomena, vol.334, pp.1-186, 2016.
DOI : 10.1016/j.physd.2016.03.007

F. Santambrogio, Optimal transport for applied mathematicians, 2015.
DOI : 10.1007/978-3-319-20828-2
URL : https://link.springer.com/content/pdf/bfm%3A978-3-319-20828-2%2F1.pdf

G. Singh, F. Memoli, T. Ishkhanov, G. Sapiro, G. Carlsson et al., Topological analysis of population activity in visual cortex, Journal of Vision, vol.8, issue.8, 2008.
DOI : 10.1167/8.8.11

C. Villani, Optimal transport : old and new, 2009.
DOI : 10.1007/978-3-540-71050-9

A. Zomorodian and G. Carlsson, Computing Persistent Homology, Discrete & Computational Geometry, vol.33, issue.2, pp.249-274, 2005.
DOI : 10.1007/s00454-004-1146-y
URL : http://graphics.stanford.edu/~afra/papers/socg04/persistence.ps.gz