Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Multiple Regression

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...

Multicompartment Models: Overview

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...

Collisions in Multiple Dimensions: Introduction

Collisions in Multiple Dimensions: Introduction

It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a problem,...

Functions of Three or More Variables

Functions of Three or More Variables

A function of three variables assigns a single real number to each point in three-dimensional space. Every point is identified by its Cartesian coordinates, x, y, and z, and the function maps this ordered triple to a scalar value. Such functions are commonly used to describe physical quantities that vary throughout space.A representative example is the electric potential generated by a point charge. In this case, the potential at a given location depends only on the distance from the charge. If...

Gaussian Elimination: Problem Solving

Gaussian Elimination: Problem Solving

Systems of linear equations in several variables are pivotal in modeling complex scenarios involving multiple unknowns and constraints. Such systems are widely used in various fields to represent relationships where several conditions must be simultaneously satisfied. Each variable in the system corresponds to an unknown quantity, while each equation imposes a linear constraint, leading to a structured approach for analyzing and solving real-world problems.A system of three equations with three...

You might also read

Related Articles

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

Sort by

Same author

Methods, design, and initial results of an angiographic core lab from VOYAGER-PAD.

Vascular medicine (London, England)·2024

Same author

Surveillance and risk factors for early restenosis following transcarotid artery revascularization.

Journal of vascular surgery·2023

Same author

Readmission after early thoracic endovascular aortic repair versus medical management of acute type B aortic dissection.

Journal of vascular surgery·2023

Same author

Anti-coagulation management in pediatric traumatic vascular injuries.

Journal of pediatric surgery·2019

Same author

Molecular clustering and percolation characteristics near the glass transition in aqueous trehalose and choline dihydrogen phosphate solutions.

Physical chemistry chemical physics : PCCP·2018

Same author

High throughput nonparametric probability density estimation.

PloS one·2018

Same journal

Large-scale Neuroanatomical Visualization Using a Manifold Embedding Approach.

Proceedings. IEEE Symposium on Visual Analytics Science and Technology·2011

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 10, 2026

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Multivariate Visual Explanation for High Dimensional Datasets.

Scott Barlowe¹, Tianyi Zhang, Yujie Liu

¹Dept of Computer Science, University of North Carolina at Charlotte.

Proceedings. IEEE Symposium on Visual Analytics Science and Technology

|August 10, 2010

Summary

This summary is machine-generated.

This study introduces a new visual explanation approach for multivariate data analysis. It enhances the discovery of complex relationships across many dimensions, improving model construction and knowledge discovery.

More Related Videos

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Related Experiment Videos

Last Updated: Jun 10, 2026

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Area of Science:

Data Science
Computer Science
Statistics

Background:

Multivariate data analysis is crucial for understanding complex datasets.
Current visualization systems struggle with high-dimensional data, limiting relationship discovery.
Effective tools are needed for exploring intricate patterns in numerous variables.

Purpose of the Study:

To present a novel visual explanation approach for multivariate data analysis.
To enable interactive discovery of relationships among a large number of dimensions.
To improve the efficiency of multivariate analysis model construction and knowledge discovery.

Main Methods:

Integration of automatic numerical differentiation techniques with multidimensional visualization.
Development of an interactive workflow for dimension reduction.
Leveraging automatic multivariate analysis and interactive visual exploration.

Main Results:

Demonstrated effectiveness in discovering multivariate relationships across high dimensions.
Provided an efficient workflow for model construction and interactive dimension reduction.
Case studies and a user study validated the approach's utility.

Conclusions:

The proposed approach significantly enhances the exploration of multivariate relationships.
It offers an effective solution for knowledge discovery in high-dimensional data.
Combines automated analysis with interactive visualization for superior insights.