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

On feature extraction via kernels.

Cheng Yang1, Liwei Wang, Jufu Feng

  • 1State Key Laboratory of Machine Perception, Department of Machine Intelligence, School of Electronics Engineering and Computer Sciences, Peking University, Beijing, China. yangch@cis.pku.edu.cn

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|March 20, 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

The Aqueous Extract of <i>Gynura divaricata</i> (L.) DC. Improves Glucose and Lipid Metabolism and Ameliorates Type 2 Diabetes Mellitus.

Evidence-based complementary and alternative medicine : eCAM·2018
Same author

A 3D-printed self-propelled, highly sensitive mini-motor for underwater pesticide detection.

Talanta·2018
Same author

Integrated analysis of microarray data to identify the genes critical for the rupture of intracranial aneurysm.

Oncology letters·2018
Same author

Editorial: Recent Advances of Cell and Gene Therapy in Kidney Diseases.

Current gene therapy·2018
Same author

Photocatalytic Supramolecular Enantiodifferentiating Dimerization of 2-Anthracenecarboxylic Acid through Triplet-Triplet Annihilation.

Organic letters·2018
Same author

On the realization of acoustic attenuation using a microperforated panel alone.

The Journal of the Acoustical Society of America·2018
Same journal

Strategic Ability Updating in Concurrent Games by Coalitional Commitment.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2015
Same journal

Meta-Analysis of the First Facial Expression Recognition Challenge.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

Adjustable model-based fusion method for multispectral and panchromatic images.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

Face Feature Weighted Fusion Based on Fuzzy Membership Degree for Video Face Recognition.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

A New Adaptive Fast Cellular Automaton Neighborhood Detection and Rule Identification Algorithm.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

Human-arm-and-hand-dynamic model with variability analyses for a stylus-based haptic interface.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
See all related articles

The kernel trick and kernels-as-features methods create nonlinear feature spaces for linear algorithms. Rigorous analysis shows these two kernel approaches are equivalent for feature extraction, improving understanding of kernel methods.

Area of Science:

  • Machine Learning
  • Kernel Methods
  • Feature Extraction

Background:

  • Kernel methods enable linear algorithms to operate in nonlinear feature spaces.
  • Two primary conceptualizations exist: the kernel trick and kernels-as-features.

Purpose of the Study:

  • To investigate the relationship between the kernel trick and kernels-as-features.
  • To analyze their application in feature extraction algorithms like Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), and Canonical Correlation Analysis (CCA).

Main Methods:

  • Theoretical analysis of kernel methods.
  • Application of kernel concepts to established feature extraction algorithms.

Main Results:

  • Demonstrated the equivalence of the kernel trick and kernels-as-features.

Related Experiment Videos

  • Showed this equivalence holds up to feature-specific scaling differences.
  • Provided a rigorous theoretical foundation for this equivalence.

    • Conclusions:

    • The two kernel conceptualizations are fundamentally equivalent for feature extraction.
    • This equivalence offers deeper insights into the mechanics of kernel-based learning.
    • Facilitates a unified understanding of nonlinear feature extraction techniques.