Using the Kriging Correlation for unsupervised feature selection problems

This paper proposes a KC Score to measure feature importance in clustering analysis of high-dimensional data. The KC Score evaluates the contribution of features based on the correlation between the original features and the reconstructed features in the low dimensional latent space. A KC Score-base...

Descripción completa

Detalles Bibliográficos
Autores principales: Chua, Cheng-Han, Guo, Meihui, Huang, Shih-Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9263137/
https://www.ncbi.nlm.nih.gov/pubmed/35798813
http://dx.doi.org/10.1038/s41598-022-15529-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9263137/
https://www.ncbi.nlm.nih.gov/pubmed/35798813
http://dx.doi.org/10.1038/s41598-022-15529-4

Ejemplares similares