Towards a quantitative survey of dimension reduction techniques (2021)
- Authors:
- USP affiliated authors: HIRATA, NINA SUMIKO TOMITA - IME ; ESPADOTO, MATEUS - IME
- Unidade: IME
- DOI: 10.1109/tvcg.2019.2944182
- Assunto: APRENDIZADO COMPUTACIONAL
- Keywords: Dimensionality reduction; quality metrics; benchmarking; quantitative analysis; design space
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: IEEE Transactions on Visualization and Computer Graphics
- ISSN: 1077-2626
- Volume/Número/Paginação/Ano: v. 27, n. 3, p. 2153-2173, 2021
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
ESPADOTO, Mateus et al. Towards a quantitative survey of dimension reduction techniques. IEEE Transactions on Visualization and Computer Graphics, v. 27, n. 3, p. 2153-2173, 2021Tradução . . Disponível em: https://doi.org/10.1109/tvcg.2019.2944182. Acesso em: 06 out. 2024. -
APA
Espadoto, M., Martins, R. M., Kerren, A., Hirata, N. S. T., & Telea, A. C. (2021). Towards a quantitative survey of dimension reduction techniques. IEEE Transactions on Visualization and Computer Graphics, 27( 3), 2153-2173. doi:10.1109/tvcg.2019.2944182 -
NLM
Espadoto M, Martins RM, Kerren A, Hirata NST, Telea AC. Towards a quantitative survey of dimension reduction techniques [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2021 ; 27( 3): 2153-2173.[citado 2024 out. 06 ] Available from: https://doi.org/10.1109/tvcg.2019.2944182 -
Vancouver
Espadoto M, Martins RM, Kerren A, Hirata NST, Telea AC. Towards a quantitative survey of dimension reduction techniques [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2021 ; 27( 3): 2153-2173.[citado 2024 out. 06 ] Available from: https://doi.org/10.1109/tvcg.2019.2944182 - Deep learning multidimensional projections
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- Improving deep learning projections by neighborhood analysis
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Informações sobre o DOI: 10.1109/tvcg.2019.2944182 (Fonte: oaDOI API)
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