0000000000350450

AUTHOR

E. And Laparra

showing 1 related works from this author

Optimized Kernel Entropy Components

2016

This work 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 of the importance of kernel eigenvectors by entropy instead of by variance as in Kernel Principal Components Analysis. In this work, we propose an extension of the KECA method, named Optimized KECA (OKECA), that directly extracts the optimal features retaining most of the data entropy by means of compacting the information in very few features (often in just one or two). The proposed method produces features which have higher expressive power. In particular…

FOS: Computer and information sciencesComputer Networks and CommunicationsKernel density estimationMachine Learning (stat.ML)02 engineering and technologyKernel principal component analysisMachine Learning (cs.LG)Artificial IntelligencePolynomial kernelStatistics - Machine Learning0202 electrical engineering electronic engineering information engineeringMathematicsbusiness.industry020206 networking & telecommunicationsPattern recognitionComputer Science ApplicationsComputer Science - LearningKernel methodKernel embedding of distributionsVariable kernel density estimationRadial basis function kernelKernel smoother020201 artificial intelligence & image processingArtificial intelligencebusinessSoftwareIEEE Transactions on Neural Networks and Learning Systems
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