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

Coefficient of Correlation01:12

Coefficient of Correlation

7.6K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
7.6K
Correlation and Regression00:53

Correlation and Regression

2.8K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
2.8K
Correlation01:09

Correlation

13.9K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
13.9K
Correlations02:20

Correlations

35.4K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
35.4K
Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

1.2K
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...
1.2K
2D NMR: Overview of Heteronuclear Correlation Techniques01:18

2D NMR: Overview of Heteronuclear Correlation Techniques

534
Heteronuclear correlation spectroscopy is an analytical technique that investigates the coupling between different types of nuclei, often a proton and an X-nucleus, such as carbon-13 or nitrogen-15. This method is commonly used in nuclear magnetic resonance (NMR) spectroscopy to gain insights into complex chemical compounds' structural and compositional aspects. A typical heteronuclear correlation spectrum displays X-nucleus chemical shifts on one axis and a proton spectrum on the other...
534

You might also read

Related Articles

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

Sort by
Same author

Arterial blood gas analysis in risk stratification of acute carbon monoxide poisoning.

The Indian journal of medical research·2026
Same author

USP15 stabilizes c-Myc to drive sunitinib resistance by suppressing cuproptosis in clear cell renal cell carcinoma.

Cell reports·2026
Same author

Comparative analysis of soil microbial diversity and chemical properties in different Phallus dongsun habitats.

Scientific reports·2026
Same author

Correction: Nanomaterial-enabled drug delivery systems for circadian medicine: bridging direct rhythm modulation and chronotherapy.

RSC advances·2025
Same author

Eco-friendly production of AgNPs by ultrasound-intensified continuous method, and process evaluation via life cycle assessment and machine learning.

Ultrasonics sonochemistry·2025
Same author

Nanomaterial-enabled drug delivery systems for circadian medicine: bridging direct rhythm modulation and chronotherapy.

RSC advances·2025
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Nov 27, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.8K

Multi-Label Feature Selection Based on High-Order Label Correlation Assumption.

Ping Zhang1,2, Wanfu Gao1,2,3, Juncheng Hu1,2

  • 1College of Computer Science and Technology, Jilin University, Changchun 130012, China.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new multi-label feature selection method (MCMFS) that accounts for high-order label correlations. MCMFS outperforms existing methods by better selecting features relevant to diverse label groups.

Keywords:
Max-Correlationinformation theorymulti-label feature selectionmulti-label learning

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.8K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.2K

Related Experiment Videos

Last Updated: Nov 27, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.8K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.8K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.2K

Area of Science:

  • Machine Learning
  • Data Science
  • Artificial Intelligence

Background:

  • Multi-label learning faces challenges with high-dimensional data and complex label dependencies.
  • Existing feature selection methods often overlook high-order label correlations, impacting performance.
  • Label correlations can be grouped, with similar labels clustering together.

Purpose of the Study:

  • To address limitations in current multi-label feature selection techniques.
  • To propose a novel method that captures high-order label correlations.
  • To improve classification performance in multi-label learning scenarios.

Main Methods:

  • A novel Max-Correlation term is introduced to capture high-order label correlations.
  • The Max-Correlation term is combined with a feature redundancy term.
  • A new algorithm, Multi-label Feature Selection considering Max-Correlation (MCMFS), is developed.

Main Results:

  • MCMFS effectively considers high-order label correlations, unlike previous methods.
  • The method ensures selected features are relevant to distinct label groups.
  • Experimental results show MCMFS's superior classification performance.

Conclusions:

  • MCMFS offers a significant advancement in multi-label feature selection.
  • Accounting for high-order label correlations is crucial for effective feature selection.
  • The proposed method enhances classification accuracy in multi-label learning.