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

Low-Rank Embedding for Robust Image Feature Extraction.

Wai Keung Wong, Zhihui Lai, Jiajun Wen

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |April 15, 2017
    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

    Expert-Guided Cross-View Fusion With Self-Derived Lesion Proposals for Multi-View Diabetic Retinopathy Grading.

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

    Morphological and Compositional Features of Lumbar Multifidus in Athletes with Chronic Non-specific Low Back Pain: A Systematic Review and Meta-Analysis.

    International journal of sports physical therapy·2026
    Same author

    Single-Injection Liposomal Bupivacaine Adductor Canal Block for Pain Control and Recovery After Total Knee Arthroplasty: A Randomized Controlled Double‑Blinded Study.

    Drug design, development and therapy·2026
    Same author

    The impact of early allograft dysfunction severity on graft and recipient outcomes in pediatric liver transplantation.

    Annals of hepatology·2026
    Same author

    Multi-view Hilbert Curve-based Hierarchical Information Aggregation for Incomplete Multimodal Alzheimer's Disease Diagnosis.

    IEEE transactions on medical imaging·2026
    Same author

    An Efficient Regenerated Cross-Modal Hashing: Improving Existing Hash Codes with the Arbitrary Length.

    IEEE transactions on pattern analysis and machine intelligence·2026
    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 Low-Rank Embedding (LRE), a new method for robust linear dimensionality reduction that effectively handles corrupted data. LRE enhances image feature extraction by uncovering relationships among images, outperforming existing techniques.

    Area of Science:

    • Computer Science
    • Machine Learning
    • Data Science

    Background:

    • Linear dimensionality reduction methods often fail with noisy or corrupted data.
    • Existing techniques struggle when sample-specific corruptions destroy data structures.

    Purpose of the Study:

    • To develop an unsupervised robust linear dimensionality reduction technique for corrupted data.
    • To enhance image feature extraction robustness against occlusions and corruptions.

    Main Methods:

    • Introduced robust low-rank representation (LRR) for robust dimensionality reduction.
    • Proposed Low-Rank Embedding (LRE) to simultaneously find optimal LRR and subspace.
    • Utilized alternating Lagrangian multiplier method and eigendecomposition for model solving.

    Related Experiment Videos

    Main Results:

    • LRE provides a robust image representation, uncovering relationships among images.
    • The method effectively reduces the negative influence of occlusions and corruptions.
    • Experiments demonstrate LRE's superiority over previous feature extraction methods on corrupted datasets.

    Conclusions:

    • LRE significantly enhances the robustness of image feature extraction.
    • The proposed method offers a robust solution for linear dimensionality reduction on corrupted data.
    • LRE shows strong performance and robustness in various corruption scenarios.