Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Joint entropy maximization in kernel-based topographic maps.

Marc M Van Hulle1

  • 1K. U. Leuven, Laboratorium voor Neuro- en Psychofysiologie, Belgium. marc@neuro.kuleuven.ac.be

Neural Computation
|August 16, 2002
PubMed
Summary
This summary is machine-generated.

A novel learning algorithm enhances kernel-based topographic maps by maximizing joint entropy. This method optimizes kernel parameters to reduce redundancy, improving map formation for unsupervised learning tasks.

Related Experiment Videos

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Correction: EEG-based classification of alzheimer's disease and frontotemporal dementia using functional connectivity.

Scientific reports·2026
Same author

The electrophysiological basis of resting-state fMRI hyperconnectivity in early Alzheimer's disease.

Alzheimer's research & therapy·2026
Same author

A Cross-Subject Band-Power Complexity Metric for Detecting Mental Fatigue Through EEG.

Brain sciences·2026
Same author

Word classification across speech modes from low-density electrocorticography signals.

Journal of neural engineering·2026
Same author

EEG-based classification of alzheimer's disease and frontotemporal dementia using functional connectivity.

Scientific reports·2026
Same author

Early aperiodic EEG changes in preclinical and prodromal Alzheimer's disease.

Alzheimer's research & therapy·2026
Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026
Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026
Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026
Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026
Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026
Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026
See all related articles

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Information Theory

Background:

  • Kernel-based topographic maps are unsupervised learning methods for data visualization and clustering.
  • Existing methods face challenges in optimizing kernel parameters and managing output redundancy.

Purpose of the Study:

  • To introduce a new learning algorithm for kernel-based topographic map formation.
  • To optimize kernel parameters by maximizing joint entropy and minimizing mutual information.

Main Methods:

  • The algorithm adjusts kernel parameters individually to maximize joint entropy of kernel outputs.
  • It maximizes differential entropies of individual kernel outputs while minimizing mutual information between them.
  • The (radial) incomplete gamma distribution is used as a kernel for maximal differential entropy with Gaussian input density.

Main Results:

  • A new clustering algorithm is proposed, using theoretically optimal joint entropy for nonoverlapping Gaussian mixture densities as a "null" distribution.
  • The learning algorithm demonstrates similarity to stochastic gradient descent on Kullback-Leibler divergence for heteroskedastic Gaussian mixture models.

Conclusions:

  • The proposed algorithm offers an effective approach to kernel-based topographic map formation.
  • It provides a principled way to optimize map parameters for improved data representation and clustering.