{"article_type":"original","status":"public","keyword":["Applied Mathematics","Artificial Intelligence","Computational Theory and Mathematics","Computer Vision and Pattern Recognition","Software"],"file":[{"creator":"dernst","access_level":"open_access","date_updated":"2024-01-08T10:09:14Z","success":1,"file_size":2370988,"date_created":"2024-01-08T10:09:14Z","checksum":"465c28ef0b151b4b1fb47977ed5581ab","file_name":"2023_IEEEToP_Ali.pdf","content_type":"application/pdf","relation":"main_file","file_id":"14740"}],"_id":"14739","acknowledgement":"The work of Maria-Jose Jimenez, Eduardo Paluzo-Hidalgo and Manuel Soriano-Trigueros was supported in part by the Spanish grant Ministerio de Ciencia e Innovacion under Grants TED2021-129438B-I00 and PID2019-107339GB-I00, and in part by REXASI-PRO H-EU project, call HORIZON-CL4-2021-HUMAN-01-01 under Grant 101070028. The work of\r\nMaria-Jose Jimenez was supported by a grant of Convocatoria de la Universidad de Sevilla para la recualificacion del sistema universitario español, 2021-23, funded by the European Union, NextGenerationEU. The work of Vidit Nanda was supported in part by EPSRC under Grant EP/R018472/1 and in part by US AFOSR under Grant FA9550-22-1-0462. \r\nWe are grateful to the team of GUDHI and TEASPOON developers, for their work and their support. We are also grateful to Streamlit for providing extra resources to deploy the web app\r\nonline on Streamlit community cloud. We thank the anonymous referees for their helpful suggestions.","file_date_updated":"2024-01-08T10:09:14Z","type":"journal_article","date_updated":"2024-01-08T10:11:46Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa_version":"Published Version","department":[{"_id":"HeEd"}],"doi":"10.1109/tpami.2023.3308391","publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issue":"12","publication_status":"published","month":"12","date_created":"2024-01-08T09:59:46Z","publisher":"IEEE","volume":45,"date_published":"2023-12-01T00:00:00Z","quality_controlled":"1","publication_identifier":{"issn":["0162-8828"],"eissn":["1939-3539"]},"ddc":["000"],"title":"A survey of vectorization methods in topological data analysis","citation":{"ieee":"D. Ali, A. Asaad, M.-J. Jimenez, V. Nanda, E. Paluzo-Hidalgo, and M. Soriano Trigueros, “A survey of vectorization methods in topological data analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 12. IEEE, pp. 14069–14080, 2023.","chicago":"Ali, Dashti, Aras Asaad, Maria-Jose Jimenez, Vidit Nanda, Eduardo Paluzo-Hidalgo, and Manuel Soriano Trigueros. “A Survey of Vectorization Methods in Topological Data Analysis.” IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE, 2023. https://doi.org/10.1109/tpami.2023.3308391.","apa":"Ali, D., Asaad, A., Jimenez, M.-J., Nanda, V., Paluzo-Hidalgo, E., & Soriano Trigueros, M. (2023). A survey of vectorization methods in topological data analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE. https://doi.org/10.1109/tpami.2023.3308391","ama":"Ali D, Asaad A, Jimenez M-J, Nanda V, Paluzo-Hidalgo E, Soriano Trigueros M. A survey of vectorization methods in topological data analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2023;45(12):14069-14080. doi:10.1109/tpami.2023.3308391","short":"D. Ali, A. Asaad, M.-J. Jimenez, V. Nanda, E. Paluzo-Hidalgo, M. Soriano Trigueros, IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (2023) 14069–14080.","mla":"Ali, Dashti, et al. “A Survey of Vectorization Methods in Topological Data Analysis.” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 12, IEEE, 2023, pp. 14069–80, doi:10.1109/tpami.2023.3308391.","ista":"Ali D, Asaad A, Jimenez M-J, Nanda V, Paluzo-Hidalgo E, Soriano Trigueros M. 2023. A survey of vectorization methods in topological data analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(12), 14069–14080."},"intvolume":" 45","article_processing_charge":"Yes (in subscription journal)","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"has_accepted_license":"1","author":[{"full_name":"Ali, Dashti","first_name":"Dashti","last_name":"Ali"},{"full_name":"Asaad, Aras","first_name":"Aras","last_name":"Asaad"},{"full_name":"Jimenez, Maria-Jose","last_name":"Jimenez","first_name":"Maria-Jose"},{"full_name":"Nanda, Vidit","first_name":"Vidit","last_name":"Nanda"},{"first_name":"Eduardo","last_name":"Paluzo-Hidalgo","full_name":"Paluzo-Hidalgo, Eduardo"},{"full_name":"Soriano Trigueros, Manuel","id":"15ebd7cf-15bf-11ee-aebd-bb4bb5121ea8","first_name":"Manuel","orcid":"0000-0003-2449-1433","last_name":"Soriano Trigueros"}],"language":[{"iso":"eng"}],"day":"01","page":"14069-14080","year":"2023","abstract":[{"text":"Attempts to incorporate topological information in supervised learning tasks have resulted in the creation of several techniques for vectorizing persistent homology barcodes. In this paper, we study thirteen such methods. Besides describing an organizational framework for these methods, we comprehensively benchmark them against three well-known classification tasks. Surprisingly, we discover that the best-performing method is a simple vectorization, which consists only of a few elementary summary statistics. Finally, we provide a convenient web application which has been designed to facilitate exploration and experimentation with various vectorization methods.","lang":"eng"}],"oa":1}