Optimized Kernel Entropy Components.
Sign Up to like & getrecommendations! Published in 2017 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2016.2530403
Abstract: This brief addresses two main issues of the standard kernel entropy component analysis (KECA) algorithm: the optimization of the kernel decomposition and the optimization of the Gaussian kernel parameter. KECA roughly reduces to a sorting… read more here.
Keywords: optimized kernel; kernel; kernel parameter; kernel entropy ... See more keywords
Optimized Kernel Entropy Components
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Transactions on Neural Networks and Learning Systems"
DOI: 10.1109/tnnls.2016.2530403.
Abstract: This brief addresses two main issues of the standard kernel entropy component analysis (KECA) algorithm: the optimization of the kernel decomposition and the optimization of the Gaussian kernel parameter. KECA roughly reduces to a sorting… read more here.
Keywords: optimized kernel; kernel; kernel parameter; kernel entropy ... See more keywords
Clustering using kernel entropy principal component analysis and variable kernel estimator
Sign Up to like & getrecommendations! Published in 2021 at "International Journal of Electrical and Computer Engineering"
DOI: 10.11591/ijece.v11i3.pp2109-2119
Abstract: Clustering as unsupervised learning method is the mission of dividing data objects into clusters with common characteristics. In the present paper, we introduce an enhanced technique of the existing EPCA data transformation method. Incorporating the… read more here.
Keywords: kernel entropy; entropy; using kernel; entropy principal ... See more keywords