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 classification with incomplete data.

David Williams1, Xuejun Liao, Ya Xue

  • 1Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708-0291, USA. dpw@ee.duke.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|January 17, 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

High virulence sub-populations in Pseudomonas aeruginosa long-term cystic fibrosis airway infections.

BMC microbiology·2017
Same author

Defining the Role of the Stroke Physician During Endovascular Therapy of Acute Ischemic Stroke.

Stroke·2017
Same author

STROKOG (stroke and cognition consortium): An international consortium to examine the epidemiology, diagnosis, and treatment of neurocognitive disorders in relation to cerebrovascular disease.

Alzheimer's & dementia (Amsterdam, Netherlands)·2017
Same author

Validation of two risk-prediction models for recurrent falls in the first year after stroke: a prospective cohort study.

Age and ageing·2017
Same author

Population Pharmacokinetics and Immunogenicity of Adalimumab in Adult Patients with Moderate-to-Severe Hidradenitis Suppurativa.

Clinical pharmacokinetics·2017
Same author

Pregnancy Outcomes in Patients with Psoriatic Arthritis.

The Journal of rheumatology·2017
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 new logistic regression algorithm to classify incomplete data without imputation. It uses Gaussian mixture models and analytic integration for accurate classification, even with missing features.

Area of Science:

  • Machine Learning
  • Data Science
  • Statistical Modeling

Background:

  • Classification tasks often suffer from incomplete data, where feature vectors have missing values.
  • Traditional methods like imputation can introduce bias or be computationally intensive.
  • Developing robust algorithms for incomplete data is crucial for real-world applications.

Purpose of the Study:

  • To develop a supervised logistic regression algorithm for classifying incomplete data.
  • To avoid traditional imputation methods by using analytic integration.
  • To extend the supervised approach to a semisupervised framework.

Main Methods:

  • Developed a supervised logistic regression algorithm for incomplete data classification.
  • Estimated conditional density functions using Gaussian Mixture Models (GMM).

Related Experiment Videos

  • Employed Expectation-Maximization (EM) and Variational Bayesian EM (VB-EM) for parameter estimation.
  • Extended the algorithm to semisupervised learning with graph-based regularization.

    • Main Results:

    • The proposed method effectively handles incomplete data without imputation.
    • Analytic integration with estimated conditional densities proved efficient.
    • The semisupervised extension successfully utilized all data types (complete, incomplete, labeled, unlabeled).

    Conclusions:

    • The developed logistic regression algorithm offers a viable solution for incomplete data classification.
    • Gaussian Mixture Models provide effective density estimation for this approach.
    • The semisupervised extension enhances classification performance by leveraging all available data.