Criteria for choosing the number of dimensions in a principal component analysis: an empirical assessment (2020)
- Authors:
- Autor USP: SANTOS, DAVI PEREIRA DOS - ICMC
- Unidade: ICMC
- Subjects: APRENDIZADO COMPUTACIONAL; ANÁLISE MULTIVARIADA
- Keywords: Feature transformation; PCA; Number of principal components; Auto ML
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: SBC
- Publisher place: Porto Alegre
- Date published: 2020
- Source:
- Título: Anais
- Conference titles: Brazilian Symposium on Data Bases - SBBD
-
ABNT
SILVA, Renata Barbosa et al. Criteria for choosing the number of dimensions in a principal component analysis: an empirical assessment. 2020, Anais.. Porto Alegre: SBC, 2020. Disponível em: https://sol.sbc.org.br/index.php/sbbd/article/view/13632. Acesso em: 08 out. 2024. -
APA
Silva, R. B., Oliveira, D. de, Santos, D. P. dos, Santos, L. F. D., Wilson, R. E., & Bêdo, M. V. N. (2020). Criteria for choosing the number of dimensions in a principal component analysis: an empirical assessment. In Anais. Porto Alegre: SBC. Recuperado de https://sol.sbc.org.br/index.php/sbbd/article/view/13632 -
NLM
Silva RB, Oliveira D de, Santos DP dos, Santos LFD, Wilson RE, Bêdo MVN. Criteria for choosing the number of dimensions in a principal component analysis: an empirical assessment [Internet]. Anais. 2020 ;[citado 2024 out. 08 ] Available from: https://sol.sbc.org.br/index.php/sbbd/article/view/13632 -
Vancouver
Silva RB, Oliveira D de, Santos DP dos, Santos LFD, Wilson RE, Bêdo MVN. Criteria for choosing the number of dimensions in a principal component analysis: an empirical assessment [Internet]. Anais. 2020 ;[citado 2024 out. 08 ] Available from: https://sol.sbc.org.br/index.php/sbbd/article/view/13632 - Seleção de características: abordagem via redes neurais aplicada à segmentação de imagens
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