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

Incremental kernel principal component analysis.

Tat-Jun Chin1, David Suter

  • 1Department of Electrical and Computer Systems Engineering, Monash University, Victoria, Australia. tjchin@i2r.a-star.edu.sg

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|June 6, 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

Robust Model Fitting via Motion-Aware Pyramid Transformer-Guided Preference Filtering and Consensus Smoothing.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Automated abdominal aortic calcification, muscle health and incident falls: the UK Biobank Imaging Study.

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research·2026
Same author

Automated Abdominal Aortic Calcification Scores and Atherosclerotic Cardiovascular Disease in the UK Biobank Imaging Study.

JACC. Advances·2026
Same author

Automated abdominal aortic calcification scoring from vertebral fracture assessment images and fall-associated hospitalisations: the Manitoba Bone Mineral Density Registry.

GeroScience·2025
Same author

Automated abdominal aortic calcification and major adverse cardiovascular events in people undergoing osteoporosis screening: the Manitoba Bone Mineral Density Registry.

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research·2025
Same author

Simultaneous automated ascertainment of prevalent vertebral fracture and abdominal aortic calcification in clinical practice: role in fracture risk assessment.

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research·2024
Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

GoP-based Quality Enhancement on Video Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

This study introduces an incremental kernel principal component analysis (KPCA) algorithm to overcome computational limitations of standard KPCA for large datasets and online processing. The new method enhances KPCA

Area of Science:

  • Machine Learning
  • Computer Vision
  • Data Science

Background:

  • Kernel Principal Component Analysis (KPCA) offers superior performance in image-related machine learning tasks compared to traditional methods like PCA.
  • Standard KPCA implementations face scalability issues with large datasets, rendering them computationally infeasible.
  • The batch processing nature of standard KPCA restricts its applicability in scenarios requiring online data analysis.

Purpose of the Study:

  • To develop an incremental computation algorithm for KPCA to address scalability and online processing limitations.
  • To enable efficient KPCA application on large-scale and dynamic datasets.
  • To expand the potential application domains of KPCA.

Main Methods:

  • The core of the solution involves computing incremental linear PCA within the kernel-induced feature space.

Related Experiment Videos

  • Reduced-set expansions are constructed to ensure constant update speed and memory usage.
  • The algorithm is designed for efficient online updates and handling of large data volumes.

    • Main Results:

    • Experimental results demonstrate the effectiveness of the proposed incremental KPCA algorithm.
    • The approach successfully addresses the computational bottlenecks of standard KPCA.
    • The method maintains efficient performance with constant memory and update speed.

    Conclusions:

    • The incremental KPCA algorithm provides an effective solution for computationally intensive KPCA tasks.
    • This advancement broadens the applicability of KPCA in large-scale and real-time machine learning scenarios.
    • The developed method offers a scalable and efficient alternative for kernel-based dimensionality reduction.