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L1-KECA employs L1-norm and a rotation matrix to maximize information potential of the input data. Additionally, in order to find the optimal extraction of ...
Kernel entropy component analysis (KECA) is a recently proposed dimensionality reduction approach, which has showed superiority in many pattern analysis ...
Kernel entropy component analysis (KECA) is a newly proposed dimensionality reduction (DR) method, which has showed superiority in many pattern analysis issues ...
Kernel entropy component analysis (KECA) is a recently proposed dimensionality reduction approach, which has showed superiority in many pattern analysis ...
Oct 14, 2018 · KECA-L1 aims to find a more robust kernel decomposition matrix such that the extracted features retain information potential as much as possible ...
Kernel entropy component analysis (KECA) is a recently proposed dimensionality reduction approach, which has showed superiority in many pattern analysis ...
Oct 14, 2018 · KECA-L1 aims to find a more robust kernel decomposition matrix such that the extracted features retain information potential as much as possible.
Kernel entropy component analysis (KECA) is a newly proposed dimensionality reduction (DR) method, which has showed superiority in many pattern analysis ...
Kernel entropy component analysis (KECA) is a newly proposed dimensionality reduction (DR) method, which has showed superiority in many pattern analysis ...
L1 norm based KPCA for novelty detection | Semantic Scholar
www.semanticscholar.org › paper › L1-n...
This work proposes a robust loss function which combined with $l_{2,1}$ regularization for KPCA, inspired by sparse PCA via variable projection, ...