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 Concept Videos

Uncertainty: Overview00:59

Uncertainty: Overview

999
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
999
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

4.8K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
4.8K
Divergence and Stokes' Theorems01:06

Divergence and Stokes' Theorems

2.0K
The divergence and Stokes' theorems are a variation of Green's theorem in a higher dimension. They are also a generalization of the fundamental theorem of calculus. The divergence theorem and Stokes' theorem are in a way similar to each other; The divergence theorem relates to the dot product of a vector, while Stokes' theorem relates to the curl of a vector. Many applications in physics and engineering make use of the divergence and Stokes' theorems, enabling us to write...
2.0K
Divergence and Curl01:15

Divergence and Curl

2.0K
The divergence of a vector field at a point is the net outward flow of the flux out of a small volume through a closed surface enclosing the volume, as the volume tends to zero. More practically, divergence measures how much a vector field spreads out or diverges from a given point. For an outgoing flux, conventionally, the divergence is positive. The diverging point is often called the "source" of the field. Meanwhile, the negative divergence of a vector field at a point means that the...
2.0K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.1K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.1K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

897
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
897

You might also read

Related Articles

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

Sort by
Same author

[Chemical constituents from a Tibetan medicine Meconopsis horridula].

Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica·2014
Same author

[Chemical constituents from whole plants of Aconitum tanguticum (III)].

Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica·2014
Same author

Plasma metabonomics study on Chinese medicine syndrome evolution of heart failure rats caused by LAD ligation.

BMC complementary and alternative medicine·2014
Same author

A versatile complementation assay for cell-to-cell and long distance movements by cucumber mosaic virus based agro-infiltration.

Virus research·2014
Same author

Composition of EPS fractions from suspended sludge and biofilm and their roles in microbial cell aggregation.

Chemosphere·2014
Same author

CXCL9 and CXCL10 accelerate acute transplant rejection mediated by alloreactive memory T cells in a mouse retransplantation model.

Experimental and therapeutic medicine·2014

Related Experiment Video

Updated: Sep 18, 2025

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.3K

Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures.

Zhipeng Xue, Yan Zhang, Ming Li

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 23, 2025
    PubMed
    Summary
    This summary is machine-generated.

    KPHD-Net enhances multi-view learning by using Proper Hölder divergence and Dempster-Shafer evidence theory for reliable data integration and decision-making, outperforming existing methods in classification and clustering.

    More Related Videos

    Quantifying Intermembrane Distances with Serial Image Dilations
    07:45

    Quantifying Intermembrane Distances with Serial Image Dilations

    Published on: September 28, 2018

    6.5K
    Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics
    14:14

    Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics

    Published on: April 16, 2017

    11.7K

    Related Experiment Videos

    Last Updated: Sep 18, 2025

    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
    07:05

    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

    Published on: October 27, 2016

    9.3K
    Quantifying Intermembrane Distances with Serial Image Dilations
    07:45

    Quantifying Intermembrane Distances with Serial Image Dilations

    Published on: September 28, 2018

    6.5K
    Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics
    14:14

    Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics

    Published on: April 16, 2017

    11.7K

    Area of Science:

    • Machine Learning
    • Data Science
    • Computer Vision

    Background:

    • Existing multi-view methods fuse data from different sources but struggle with noisy data and domain gaps.
    • Current uncertainty estimation often uses Kullback-Leibler (KL) divergence, which overlooks modality-specific differences.

    Purpose of the Study:

    • To introduce KPHD-Net, a novel approach for robust multi-view classification and clustering.
    • To improve the reliability of multi-view integration and decision-making, especially with imperfect data.

    Main Methods:

    • KPHD-Net utilizes a variational Dirichlet distribution for class probabilities and integrates evidence from multiple views.
    • It employs Proper Hölder divergence for accurate discrepancy measurement and Dempster-Shafer evidence theory (DST) for enhanced uncertainty estimation.
    • The framework incorporates a Kalman filter with DST for improved future state estimation and fusion reliability.

    Main Results:

    • Theoretical analysis confirms Proper Hölder divergence as a superior measure for distribution discrepancies in multi-view learning.
    • KPHD-Net demonstrates significantly improved accuracy, robustness, and reliability compared to state-of-the-art methods.
    • Experimental results validate the effectiveness of the proposed approach in both classification and clustering tasks.

    Conclusions:

    • KPHD-Net offers a theoretically grounded and practically effective solution for challenging multi-view learning problems.
    • The integration of Hölder divergence and Dempster-Shafer evidence theory provides robust uncertainty estimation and data fusion.
    • The proposed method sets a new benchmark for performance in multi-view classification and clustering applications.