Extraction of Important Factors in a High-Dimensional Data Space: An Application for High-Growth Firms

We introduce a new non-black-box method of extracting multiple areas in a high-dimensional big data space where data points that satisfy specific conditions are highly concentrated. First, we extract one-dimensional areas where the data that satisfy specific conditions are mostly gathered by using t...

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Detalles Bibliográficos
Autores principales: Wada, Takuya, Takayasu, Hideki, Takayasu, Misako
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10047971/
https://www.ncbi.nlm.nih.gov/pubmed/36981376
http://dx.doi.org/10.3390/e25030488

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10047971/
https://www.ncbi.nlm.nih.gov/pubmed/36981376
http://dx.doi.org/10.3390/e25030488

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