Kernel Entropy Component Analysis with Nongreedy L1-Norm Maximization

Kernel entropy component analysis (KECA) is a newly proposed dimensionality reduction (DR) method, which has showed superiority in many pattern analysis issues previously solved by principal component analysis (PCA). The optimized KECA (OKECA) is a state-of-the-art variant of KECA and can return pro...

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Detalles Bibliográficos
Autores principales: Ji, Haijin, Huang, Song
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2018
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6204191/
https://www.ncbi.nlm.nih.gov/pubmed/30405708
http://dx.doi.org/10.1155/2018/6791683

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6204191/
https://www.ncbi.nlm.nih.gov/pubmed/30405708
http://dx.doi.org/10.1155/2018/6791683

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