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

Instance-Level Cost-Sensitive Hypergraph Learning with Quality-Aware Structure Judgement for Anomaly Detection.

Nan Wang, Xibin Zhao, Yue Gao

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |June 2, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Related Concept Videos

    Detection of Gross Error: The Q Test01:00

    Detection of Gross Error: The Q Test

    When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
    Structural Classification of Joints01:20

    Structural Classification of Joints

    Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
    A fibrous joint is where the adjacent bones are united by fibrous connective...

    You might also read

    Related Articles

    Articles linked to this work by shared authors, journal, and citation graph.

    Sort by
    Same author

    Twist-1, a novel regulator of hematopoietic stem cell self-renewal and myeloid lineage development.

    Stem cells (Dayton, Ohio)·2014
    Same author

    A 7.81 W 355 nm ultraviolet picosecond laser using La2CaB10O19 as a nonlinear optical crystal.

    Optics express·2014
    Same author

    Population pharmacokinetics of intravenous levofloxacin 500 mg/day dosage in infected patients.

    Die Pharmazie·2014
    Same author

    ACE2 activity was increased in atherosclerotic plaque by losartan: Possible relation to anti-atherosclerosis.

    Journal of the renin-angiotensin-aldosterone system : JRAAS·2014
    Same author

    An independent study of the preclinical efficacy of C2-8 in the R6/2 transgenic mouse model of Huntington's disease.

    Journal of Huntington's disease·2014
    Same author

    Benefit-risk assessment in a post-market setting: a case study integrating real-life experience into benefit-risk methodology.

    Pharmacoepidemiology and drug safety·2014
    Same journal

    Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

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

    RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

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

    CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

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

    DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

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

    Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

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

    Learning Shape Anchors for Holistic Indoor Scene Understanding.

    IEEE transactions on pattern analysis and machine intelligence·2026
    See all related articles

    This study introduces a novel instance-level cost-sensitive hypergraph learning method (ICSHL) for anomaly detection. ICSHL enhances hypergraph quality and instance importance for superior abnormal pattern identification.

    Area of Science:

    • Data Science
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Anomaly detection identifies unusual data points deviating from normal patterns.
    • Hypergraph methods are effective for anomaly detection due to their ability to model complex relationships.
    • Existing hypergraph methods face challenges with varying instance influence and hypergraph quality.

    Purpose of the Study:

    • To propose an instance-level cost-sensitive hypergraph learning method (ICSHL) for anomaly detection.
    • To address the limitations of varying instance importance and hypergraph quality in existing methods.
    • To improve the accuracy and robustness of anomaly detection using enhanced hypergraph learning.

    Main Methods:

    • Developed an instance-level cost-sensitive hypergraph learning framework (ICSHL).

    Related Experiment Videos

  • Integrated instance-level cost information into hypergraph construction to reflect varying sample importance.
  • Incorporated quality-aware structural judgment to preserve hypergraph integrity by emphasizing high-margin structures.

    • Main Results:

    • ICSHL effectively captures varying sample importance through instance-level cost sensitivity.
    • The quality-aware structural judgment enhances the reliability of the constructed hypergraph.
    • Extensive experiments on three datasets demonstrate the superiority of ICSHL over state-of-the-art methods.

    Conclusions:

    • The proposed ICSHL method offers a significant advancement in hypergraph-based anomaly detection.
    • Instance-level cost sensitivity and quality-aware structural judgment are crucial for effective anomaly detection.
    • ICSHL provides a robust and effective solution for identifying anomalies in complex datasets.