Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives

By José Carlos Príncipe. 2010

482 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c46f4c41bfba6b962c5ec01d40b8b2962fd73cd8

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Citations in the corpus

A Generalized Methodology for Data Analysis (2018, background): “A more recent alternative is to approximate the distributions using nonparametric, data-centered functions, such as particle filters [9], entropy-based information theoretic learning (ITL) [5], etc.”
Families of Alpha- Beta- and Gamma- Divergences: Flexible and Robust Measures of Similarities (2010, background): “For γ = 2, the asymmetric Gamma-divergences (equal to a symmetric Gamma-divergence) is reduced to Cauchy–Schwarz divergence, introduced by Principe [83]”
A review of feature selection methods based on mutual information (2013, methodology): “Since then, information theory principles have been largely incorporated into machine learning, see for example Principe [47].”
UNCERTAINTY MODELING AND INTERPRETABILITY IN CONVOLUTIONAL NEURAL NETWORKS FOR POLYP SEGMENTATION (2018, unknown): “Another promising approach proposed during the last couple of months is using ITL [88] for analyzing DNNs [83].”
Generalized Correntropy for Robust Adaptive Filtering (2015, background): “Typical examples include mixed-norm [9], [10], M-estimate cost [11], [12], and error entropy [13]–[18].”
Steady-State Mean-Square Error Analysis for Adaptive Filtering under the Maximum Correntropy Criterion (2014, methodology): “In particular, the maximum correntropy criterion (MCC), which is a robust optimality criterion for non-Gaussian signal processing [3]–[9], has recently been successfully applied in adaptive filtering [10], [11].”