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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...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Hindawi
2018
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| 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 |