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

Mercer kernel-based clustering in feature space.

M Girolami1

  • 1Lab. of Comput. and Inf. Sci., Helsinki Univ. of Technol.

IEEE Transactions on Neural Networks
|February 5, 2008
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

Two-Photon Sub-Bandgap Photocurrent in Surface-Nanotextured Black Diamond Films for Solar Energy Conversion.

ACS photonics·2025
Same author

Management of symptomatic spine metastases: A multidisciplinary approach based flow-chart.

Revista espanola de cirugia ortopedica y traumatologia·2023
Same author

Management of symptomatic spine metastases: A multidisciplinary approach based flow-chart.

Revista espanola de cirugia ortopedica y traumatologia·2023
Same author

A high-resolution neutron spectroscopic camera for the SPARC tokamak based on the Jet European Torus deuterium-tritium experience.

The Review of scientific instruments·2022
Same author

Carbon Ion Radiotherapy for sacral chordoma: a systematic review of the literature.

Journal of biological regulators and homeostatic agents·2020
Same author

Novel concepts and nanostructured materials for thermionic-based solar and thermal energy converters.

Nanotechnology·2020
Same journal

Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

IEEE transactions on neural networks·2013
Same journal

Guest editorial: special section on white box nonlinear prediction models.

IEEE transactions on neural networks·2011
Same journal

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

IEEE transactions on neural networks·2011
Same journal

Guest editorial: special section on data-based control, modeling, and optimization.

IEEE transactions on neural networks·2011
Same journal

Neural network-based multiple robot simultaneous localization and mapping.

IEEE transactions on neural networks·2011
Same journal

Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

IEEE transactions on neural networks·2011
See all related articles

This study introduces a novel method for unsupervised data clustering and determining the number of clusters. By transforming data into a higher dimensional space, it enhances pattern separation and simplifies analysis.

Area of Science:

  • Data Science
  • Machine Learning
  • Pattern Recognition

Background:

  • Unsupervised data partitioning is crucial for understanding complex datasets.
  • Estimating the inherent number of clusters is a significant challenge in data analysis.
  • Nonlinear data transformations can improve the linear separability of patterns.

Purpose of the Study:

  • To develop a method for unsupervised data partitioning.
  • To provide a technique for estimating the number of inherent data clusters.
  • To simplify data structure analysis through feature space transformation.

Main Methods:

  • Employing nonlinear data transformation into a high-dimensional feature space.
  • Utilizing eigenvectors of a kernel matrix for cluster number estimation.

Related Experiment Videos

  • Implementing a computationally efficient iterative procedure for feature space partitioning.

    • Main Results:

    • The eigenvectors of the kernel matrix effectively estimate the number of inherent clusters.
    • The proposed iterative procedure enables simple and effective feature space partitioning.
    • Nonlinear transformation enhances the linear separability of data patterns.

    Conclusions:

    • The method offers a robust approach to unsupervised clustering and cluster number estimation.
    • Feature space transformation simplifies complex data structures for analysis.
    • This technique provides a computationally efficient solution for data partitioning.