---
_id: '14739'
abstract:
- lang: eng
  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.
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."
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Dashti
  full_name: Ali, Dashti
  last_name: Ali
- first_name: Aras
  full_name: Asaad, Aras
  last_name: Asaad
- first_name: Maria-Jose
  full_name: Jimenez, Maria-Jose
  last_name: Jimenez
- first_name: Vidit
  full_name: Nanda, Vidit
  last_name: Nanda
- first_name: Eduardo
  full_name: Paluzo-Hidalgo, Eduardo
  last_name: Paluzo-Hidalgo
- first_name: Manuel
  full_name: Soriano Trigueros, Manuel
  id: 15ebd7cf-15bf-11ee-aebd-bb4bb5121ea8
  last_name: Soriano Trigueros
  orcid: 0000-0003-2449-1433
citation:
  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. <i>IEEE Transactions
    on Pattern Analysis and Machine Intelligence</i>. 2023;45(12):14069-14080. doi:<a
    href="https://doi.org/10.1109/tpami.2023.3308391">10.1109/tpami.2023.3308391</a>
  apa: Ali, D., Asaad, A., Jimenez, M.-J., Nanda, V., Paluzo-Hidalgo, E., &#38; Soriano
    Trigueros, M. (2023). A survey of vectorization methods in topological data analysis.
    <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. IEEE. <a
    href="https://doi.org/10.1109/tpami.2023.3308391">https://doi.org/10.1109/tpami.2023.3308391</a>
  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.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>.
    IEEE, 2023. <a href="https://doi.org/10.1109/tpami.2023.3308391">https://doi.org/10.1109/tpami.2023.3308391</a>.
  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,” <i>IEEE
    Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 45, no. 12.
    IEEE, pp. 14069–14080, 2023.
  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.
  mla: Ali, Dashti, et al. “A Survey of Vectorization Methods in Topological Data
    Analysis.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>,
    vol. 45, no. 12, IEEE, 2023, pp. 14069–80, doi:<a href="https://doi.org/10.1109/tpami.2023.3308391">10.1109/tpami.2023.3308391</a>.
  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.
date_created: 2024-01-08T09:59:46Z
date_published: 2023-12-01T00:00:00Z
date_updated: 2024-01-08T10:11:46Z
day: '01'
ddc:
- '000'
department:
- _id: HeEd
doi: 10.1109/tpami.2023.3308391
file:
- access_level: open_access
  checksum: 465c28ef0b151b4b1fb47977ed5581ab
  content_type: application/pdf
  creator: dernst
  date_created: 2024-01-08T10:09:14Z
  date_updated: 2024-01-08T10:09:14Z
  file_id: '14740'
  file_name: 2023_IEEEToP_Ali.pdf
  file_size: 2370988
  relation: main_file
  success: 1
file_date_updated: 2024-01-08T10:09:14Z
has_accepted_license: '1'
intvolume: '        45'
issue: '12'
keyword:
- Applied Mathematics
- Artificial Intelligence
- Computational Theory and Mathematics
- Computer Vision and Pattern Recognition
- Software
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
page: 14069-14080
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_identifier:
  eissn:
  - 1939-3539
  issn:
  - 0162-8828
publication_status: published
publisher: IEEE
quality_controlled: '1'
status: public
title: A survey of vectorization methods in topological data analysis
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 45
year: '2023'
...
---
_id: '6554'
abstract:
- lang: eng
  text: Due to the importance of zero-shot learning, i.e. classifying images where
    there is a lack of labeled training data, the number of proposed approaches has
    recently increased steadily. We argue that it is time to take a step back and
    to analyze the status quo of the area. The purpose of this paper is three-fold.
    First, given the fact that there is no agreed upon zero-shot learning benchmark,
    we first define a new benchmark by unifying both the evaluation protocols and
    data splits of publicly available datasets used for this task. This is an important
    contribution as published results are often not comparable and sometimes even
    flawed due to, e.g. pre-training on zero-shot test classes. Moreover, we propose
    a new zero-shot learning dataset, the Animals with Attributes 2 (AWA2) dataset
    which we make publicly available both in terms of image features and the images
    themselves. Second, we compare and analyze a significant number of the state-of-the-art
    methods in depth, both in the classic zero-shot setting but also in the more realistic
    generalized zero-shot setting. Finally, we discuss in detail the limitations of
    the current status of the area which can be taken as a basis for advancing it.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Yongqin
  full_name: Xian, Yongqin
  last_name: Xian
- first_name: Christoph
  full_name: Lampert, Christoph
  id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
  last_name: Lampert
  orcid: 0000-0002-4561-241X
- first_name: Bernt
  full_name: Schiele, Bernt
  last_name: Schiele
- first_name: Zeynep
  full_name: Akata, Zeynep
  last_name: Akata
citation:
  ama: Xian Y, Lampert C, Schiele B, Akata Z. Zero-shot learning - A comprehensive
    evaluation of the good, the bad and the ugly. <i>IEEE Transactions on Pattern
    Analysis and Machine Intelligence</i>. 2019;41(9):2251-2265. doi:<a href="https://doi.org/10.1109/tpami.2018.2857768">10.1109/tpami.2018.2857768</a>
  apa: Xian, Y., Lampert, C., Schiele, B., &#38; Akata, Z. (2019). Zero-shot learning
    - A comprehensive evaluation of the good, the bad and the ugly. <i>IEEE Transactions
    on Pattern Analysis and Machine Intelligence</i>. Institute of Electrical and
    Electronics Engineers (IEEE). <a href="https://doi.org/10.1109/tpami.2018.2857768">https://doi.org/10.1109/tpami.2018.2857768</a>
  chicago: Xian, Yongqin, Christoph Lampert, Bernt Schiele, and Zeynep Akata. “Zero-Shot
    Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly.” <i>IEEE
    Transactions on Pattern Analysis and Machine Intelligence</i>. Institute of Electrical
    and Electronics Engineers (IEEE), 2019. <a href="https://doi.org/10.1109/tpami.2018.2857768">https://doi.org/10.1109/tpami.2018.2857768</a>.
  ieee: Y. Xian, C. Lampert, B. Schiele, and Z. Akata, “Zero-shot learning - A comprehensive
    evaluation of the good, the bad and the ugly,” <i>IEEE Transactions on Pattern
    Analysis and Machine Intelligence</i>, vol. 41, no. 9. Institute of Electrical
    and Electronics Engineers (IEEE), pp. 2251–2265, 2019.
  ista: Xian Y, Lampert C, Schiele B, Akata Z. 2019. Zero-shot learning - A comprehensive
    evaluation of the good, the bad and the ugly. IEEE Transactions on Pattern Analysis
    and Machine Intelligence. 41(9), 2251–2265.
  mla: Xian, Yongqin, et al. “Zero-Shot Learning - A Comprehensive Evaluation of the
    Good, the Bad and the Ugly.” <i>IEEE Transactions on Pattern Analysis and Machine
    Intelligence</i>, vol. 41, no. 9, Institute of Electrical and Electronics Engineers
    (IEEE), 2019, pp. 2251–65, doi:<a href="https://doi.org/10.1109/tpami.2018.2857768">10.1109/tpami.2018.2857768</a>.
  short: Y. Xian, C. Lampert, B. Schiele, Z. Akata, IEEE Transactions on Pattern Analysis
    and Machine Intelligence 41 (2019) 2251–2265.
date_created: 2019-06-11T14:05:59Z
date_published: 2019-09-01T00:00:00Z
date_updated: 2023-09-05T13:18:09Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/tpami.2018.2857768
external_id:
  arxiv:
  - '1707.00600'
  isi:
  - '000480343900015'
intvolume: '        41'
isi: 1
issue: '9'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/1707.00600
month: '09'
oa: 1
oa_version: Preprint
page: 2251 - 2265
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_identifier:
  eissn:
  - 1939-3539
  issn:
  - 0162-8828
publication_status: published
publisher: Institute of Electrical and Electronics Engineers (IEEE)
quality_controlled: '1'
scopus_import: '1'
status: public
title: Zero-shot learning - A comprehensive evaluation of the good, the bad and the
  ugly
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 41
year: '2019'
...