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

Ranks01:02

Ranks

471
Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
471
Retrieval01:12

Retrieval

424
Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
Recall involves accessing information without cues, such as during an essay test, where individuals must retrieve facts and concepts from memory unaided. Another example is remembering the name of a colleague...
424
Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

1.5K
Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates correlation by...
1.5K
Wilcoxon Rank-Sum Test01:21

Wilcoxon Rank-Sum Test

732
The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a nonparametric test used to determine if there is a significant difference between the distributions of two independent samples. This test is designed specifically for two independent populations and has the following key requirements:
732
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

490
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
490
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

1.0K
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of...
1.0K

You might also read

Related Articles

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

Sort by
Same author

A dataset of sugarcane crop yield, production environment, meteorological records, and satellite images of commercial fields in the northeast of São Paulo State, Brazil.

Data in brief·2026
Same author

Rank Flow Embedding for Unsupervised and Semi-Supervised Manifold Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2023
Same author

Evaluation of denoising digital breast tomosynthesis data in both projection and image domains and a study of noise model on digital breast tomosynthesis image domain.

Journal of medical imaging (Bellingham, Wash.)·2022
Same author

Automatic Markerless Motion Detector Method against Traditional Digitisation for 3-Dimensional Movement Kinematic Analysis of Ball Kicking in Soccer Field Context.

International journal of environmental research and public health·2022
Same author

Litter Detection with Deep Learning: A Comparative Study.

Sensors (Basel, Switzerland)·2022
Same author

Cognitive Control-Loop for Elastic Optical Networks with Space-Division Multiplexing.

Sensors (Basel, Switzerland)·2021
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

Related Experiment Video

Updated: Jan 23, 2026

Multimedia Battery for Assessment of Cognitive and Basic Skills in Mathematics BM-PROMA
10:58

Multimedia Battery for Assessment of Cognitive and Basic Skills in Mathematics BM-PROMA

Published on: August 28, 2021

5.0K

Multimedia Retrieval Through Unsupervised Hypergraph-Based Manifold Ranking.

Daniel Carlos Guimaraes Pedronette, Lucas Pascotti Valem, Jurandy Almeida

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 11, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel hypergraph-based manifold ranking algorithm for multimedia retrieval. The new method effectively models complex relationships, significantly improving retrieval accuracy over existing techniques.

    More Related Videos

    Retrieval of Mouse Oocytes
    08:42

    Retrieval of Mouse Oocytes

    Published on: April 28, 2007

    28.5K
    A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers
    08:05

    A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

    Published on: January 5, 2018

    10.1K

    Related Experiment Videos

    Last Updated: Jan 23, 2026

    Multimedia Battery for Assessment of Cognitive and Basic Skills in Mathematics BM-PROMA
    10:58

    Multimedia Battery for Assessment of Cognitive and Basic Skills in Mathematics BM-PROMA

    Published on: August 28, 2021

    5.0K
    Retrieval of Mouse Oocytes
    08:42

    Retrieval of Mouse Oocytes

    Published on: April 28, 2007

    28.5K
    A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers
    08:05

    A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

    Published on: January 5, 2018

    10.1K

    Area of Science:

    • Computer Science
    • Information Retrieval
    • Machine Learning

    Background:

    • Accurate ranking of multimedia objects is crucial for retrieval and learning.
    • Manifold learning methods capture intrinsic data structure but often focus on pairwise relationships.

    Purpose of the Study:

    • To propose a novel manifold ranking algorithm using hypergraphs for unsupervised multimedia retrieval.
    • To leverage hypergraphs for modeling complex similarity relationships beyond pairwise interactions.

    Main Methods:

    • Developed a hypergraph-based manifold ranking algorithm.
    • Utilized hyperedges to construct contextual data representations.
    • Derived an effective similarity function from encoded hypergraph information.

    Main Results:

    • The proposed algorithm demonstrated high effectiveness gains across nine diverse public datasets.
    • Achieved superior performance compared to state-of-the-art manifold ranking methods.
    • Validated effectiveness in various multimedia retrieval scenarios.

    Conclusions:

    • Hypergraph-based manifold ranking offers a powerful approach for unsupervised multimedia retrieval.
    • Modeling higher-order relationships via hypergraphs enhances similarity computation and ranking accuracy.
    • The proposed method provides significant improvements for multimedia information retrieval tasks.