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 Video

Updated: May 13, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Schroedinger Eigenmaps for the analysis of biomedical data.

Wojciech Czaja1, Martin Ehler

  • 1Department of Mathematics, University of Maryland, College Park, MD 20742, USA. wojtek@math.umd.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|March 23, 2013
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

Input Layer Regularization and Automated Regularization Hyperparameter Tuning for Myelin Water Estimation Using Deep Learning.

NMR in biomedicine·2026
Same author

Time-resolved atomic-resolution Brownian tomography of single nanocrystals reveals size-dependent dynamics.

Science advances·2025
Same author

Spectral Decomposition of Discrepancy Kernels on the Euclidean Ball, the Special Orthogonal Group, and the Grassmannian Manifold.

Constructive approximation·2023
Same author

Input layer regularization for magnetic resonance relaxometry biexponential parameter estimation.

Magnetic resonance in chemistry : MRC·2022
Same author

Solving 2D Fredholm Integral from Incomplete Measurements Using Compressive Sensing.

SIAM journal on imaging sciences·2021
Same author

Automated Quantification of Photoreceptor alteration in macular disease using Optical Coherence Tomography and Deep Learning.

Scientific reports·2020
Same journal

Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective.

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

Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation.

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

TraGraph-GS: Trajectory Graph-based Gaussian Splatting for Arbitrary Large-Scale Scene Rendering.

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

SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving.

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

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

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

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

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

We developed Schroedinger Eigenmaps (SE), a novel semi-supervised learning method. This technique uses graph Schroedinger operators to analyze biomedical data and retinal images effectively.

Area of Science:

  • Computational Biology
  • Machine Learning
  • Image Analysis

Background:

  • Manifold learning is crucial for analyzing complex, high-dimensional data.
  • Semi-supervised learning offers a powerful approach when labeled data is scarce.
  • Biomedical image analysis requires robust techniques for feature extraction and data recovery.

Purpose of the Study:

  • Introduce Schroedinger Eigenmaps (SE), a novel semi-supervised manifold learning and recovery technique.
  • Demonstrate the utility of SE in analyzing standard biomedical datasets.
  • Apply SE to the novel domain of multispectral retinal image analysis.

Main Methods:

  • Implementation of graph Schroedinger operators.
  • Utilizing barrier potentials as carriers of labeled information.

More Related Videos

Modeling Ligands into Maps Derived from Electron Cryomicroscopy
09:30

Modeling Ligands into Maps Derived from Electron Cryomicroscopy

Published on: July 19, 2024

En face Cryosectioning of Mouse Retina for High-dimensional Spatial Molecular Analysis
08:57

En face Cryosectioning of Mouse Retina for High-dimensional Spatial Molecular Analysis

Published on: July 8, 2025

Related Experiment Videos

Last Updated: May 13, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Modeling Ligands into Maps Derived from Electron Cryomicroscopy
09:30

Modeling Ligands into Maps Derived from Electron Cryomicroscopy

Published on: July 19, 2024

En face Cryosectioning of Mouse Retina for High-dimensional Spatial Molecular Analysis
08:57

En face Cryosectioning of Mouse Retina for High-dimensional Spatial Molecular Analysis

Published on: July 8, 2025

  • Semi-supervised manifold learning and data recovery.

    • Main Results:

    • Successfully applied SE to standard biomedical datasets.
    • Demonstrated effective analysis of new multispectral retinal images.
    • Validated the capability of SE for manifold learning and data recovery in complex datasets.

    Conclusions:

    • Schroedinger Eigenmaps (SE) provide a powerful new tool for semi-supervised learning.
    • The method shows promise for advancing biomedical data analysis, particularly in medical imaging.
    • SE offers a flexible framework for incorporating labeled information into manifold learning.