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

Correlations02:20

Correlations

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...
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the other increases, and...
Correlation of Experimental Data01:23

Correlation of Experimental Data

Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity, and...
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an organic...
Coefficient of Correlation01:12

Coefficient of Correlation

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 strength of the linear...
Correlation01:09

Correlation

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:

You might also read

Related Articles

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

Sort by
Same author

Fusion of Multi-Task fMRI Data: Guided Solutions for IVA and Transposed IVA.

Sensors (Basel, Switzerland)·2026
Same author

Adaptive constrained independent vector analysis: An effective solution for analysis of large-scale medical imaging data.

IEEE journal of selected topics in signal processing·2020
Same author

An ICA based approach for steady-state and transient analysis of task fMRI data: Application to study of thermal pain response.

Journal of neuroscience methods·2019
Same author

A new blind source separation framework for signal analysis and artifact rejection in functional Near-Infrared Spectroscopy.

NeuroImage·2019
Same author

A method to compare the discriminatory power of data-driven methods: Application to ICA and IVA.

Journal of neuroscience methods·2018
Same author

The role of diversity in data-driven analysis of multi-subject fMRI data: Comparison of approaches based on independence and sparsity using global performance metrics.

Human brain mapping·2018
Same journal

Generative Principal Component Regression via Variational Inference.

IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Domain Adaptive Bootstrap Aggregating.

IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Peak Persistence Diagrams for Shape-Based Signal Estimation.

IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society·2026
Same journal

An efficient solution to Hidden Markov Models on trees with coupled branches.

IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society·2025
Same journal

Large-Scale Independent Vector Analysis (IVA-G) via Coresets.

IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society·2025
Same journal

Learnable Filters for Geometric Scattering Modules.

IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society·2025
See all related articles

Related Experiment Video

Updated: Jun 15, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Joint Blind Source Separation by Multi-set Canonical Correlation Analysis.

Yi-Ou Li1, Tülay Adalı, Wei Wang

  • 1Y.-O. Li, T. Adali, and Wei Wang are with the Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250 USA (e-mail: liyiou1@umbc.edu ).

IEEE Transactions on Signal Processing : a Publication of the IEEE Signal Processing Society
|March 12, 2010
PubMed
Summary
This summary is machine-generated.

This study presents a new method using multi-set canonical correlation analysis (M-CCA) for joint blind source separation across multiple datasets. M-CCA effectively extracts correlated sources, outperforming other methods for complex data and fMRI analysis.

More Related Videos

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

Related Experiment Videos

Last Updated: Jun 15, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

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

Area of Science:

  • Signal Processing
  • Data Analysis
  • Machine Learning

Background:

  • Blind Source Separation (BSS) is crucial for isolating underlying signals from mixed data.
  • Existing BSS methods often struggle with multiple datasets or complex source correlations.
  • Joint BSS aims to separate corresponding sources across different datasets simultaneously.

Purpose of the Study:

  • To introduce a novel and effective scheme for joint blind source separation (BSS) of multiple datasets.
  • To develop a generative model for joint BSS based on latent source correlations.
  • To demonstrate the superiority of the proposed method over existing techniques.

Main Methods:

  • Utilizing multi-set canonical correlation analysis (M-CCA) for joint BSS.
  • Proposing a generative model that captures within- and between-dataset source correlations.
  • Defining source separability conditions for successful joint extraction.

Main Results:

  • M-CCA successfully extracts corresponding sources when separability conditions are met.
  • The M-CCA scheme shows superior performance for numerous datasets and heterogeneous correlations.
  • Effective separation of complex-valued sources with circular and non-circular distributions is achieved.

Conclusions:

  • The proposed M-CCA scheme provides a simple and effective solution for joint BSS.
  • M-CCA demonstrates robust performance across diverse and complex datasets.
  • The method is validated through its application to functional magnetic resonance imaging (fMRI) data analysis.