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

Efficiently updating and tracking the dominant kernel principal components.

L Hoegaerts1, L De Lathauwer, I Goethals

  • 1Katholieke Universiteit Leuven, Department of Electrical Engineering, ESAT-SCD-SISTA, Kasteelpark Arenberg 10, B-3001 Leuven (Heverlee), Belgium. Luc.Hoegaerts@gmail.com

Neural Networks : the Official Journal of the International Neural Network Society
|January 20, 2007
PubMed
Summary
This summary is machine-generated.

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

Cat Scratch Colon.

Acta gastro-enterologica Belgica·2025
Same author

Short communication: Circadian variations and day-to-day variability of clinical signs used for the early diagnosis of pneumonia within and between calves.

Research in veterinary science·2023
Same author

Unusual cause of low abdominal pain.

Acta gastro-enterologica Belgica·2021
Same author

Erythema multiforme in the esophagus.

Acta gastro-enterologica Belgica·2021
Same author

Is there a role for <sup>18</sup>F-FDG PET-CT in Familial Mediterranean fever? A case report and overview of the literature.

Radiology case reports·2021
Same author

Monitoring of Brain Hemodynamics Coupling in Neonates using Updated Tensor Decompositions.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2020
Same journal

TraNce: Type-aware hypergraph neural network with biological mediators for drug repositioning.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Decentralized ADMM for factorization-based Low-rank matrix estimation.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Memristive neuromorphic circuit design inspired by the neural mechanisms of conditioned fear.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Q-learning based asynchronous Boolean control networks stabilization with data loss.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

New results on prescribed-time synchronization of complex networks via intermittent control.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Variance-constrained multi-view ensemble broad network for imbalanced data.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles

This study introduces an efficient incremental method for calculating dominant kernel eigenbases, enabling dynamic tracking of kernel eigenspaces in machine learning. The approach provides stable and accurate approximations for eigenvalues and eigenvectors in large-scale, dynamic datasets.

Area of Science:

  • Machine Learning
  • Kernel Methods
  • Computational Mathematics

Background:

  • Dominant eigenvectors of kernel Gram matrices are crucial for kernel methods like PCA and denoising.
  • Traditional batch eigenvector decomposition is computationally intensive and unsuitable for dynamic or large-scale data.

Purpose of the Study:

  • To develop an efficient incremental approach for calculating the dominant kernel eigenbasis.
  • To enable dynamic tracking of kernel eigenspaces for large-scale and time-varying data.

Main Methods:

  • An efficient incremental algorithm for updating the dominant kernel eigenbasis.
  • Numerical experiments comparing the incremental approach with batch eigenvector decomposition.

Main Results:

Related Experiment Videos

  • The proposed incremental scheme allows for fast calculation of the dominant kernel eigenbasis.
  • The method provides numerically stable and accurate approximations for eigenvalues and eigenvectors at each iteration.

Conclusions:

  • The incremental approach overcomes the limitations of batch methods for dynamic and large-scale kernel computations.
  • This method enhances the applicability of kernel methods in real-world, evolving datasets.