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

Dimensional Analysis02:19

Dimensional Analysis

15.1K
The concept of dimension is important because every mathematical equation linking physical quantities must be dimensionally consistent, implying that mathematical equations must meet the following two rules. The first rule is that, in an equation, the expressions on each side of the equal sign must have the same dimensions. This is fairly intuitive since we can only add or subtract quantities of the same type (dimension). The second rule states that, in an equation, the arguments of any of the...
15.1K
Problem Solving: Dimensional Analysis01:08

Problem Solving: Dimensional Analysis

3.4K
Every mathematical equation that connects separate distinct physical quantities must be dimensionally consistent, which implies it must abide by two rules. For this reason, the concept of dimension is crucial. The first rule is that an equation's expressions on either side of an equality must have the exact same dimension, i.e., quantities of the same dimension can be added or removed. The second rule stipulates that all popular mathematical functions, such as exponential, logarithmic, and...
3.4K
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

5.3K
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...
5.3K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

129
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
129
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

367
Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
367
Correlation of Experimental Data01:23

Correlation of Experimental Data

230
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,...
230

You might also read

Related Articles

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

Sort by
Same author

Generalist foundation models from a multimodal dataset for 3D computed tomography.

Nature biomedical engineering·2026
Same author

Artificial Intelligence-Assisted Image Extraction in Neonatal Echocardiography for Congenital Heart Disease Diagnosis in Sub-Saharan Africa: Protocol for Model Development.

JMIR research protocols·2025
Same author

Whole examination AI estimation of fetal biometrics from 20-week ultrasound scans.

NPJ digital medicine·2025
Same author

ChemInformatics Model Explorer (CIME): exploratory analysis of chemical model explanations.

Journal of cheminformatics·2022
Same author

Coral: a web-based visual analysis tool for creating and characterizing cohorts.

Bioinformatics (Oxford, England)·2021
Same author

Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning.

NPJ digital medicine·2021
Same journal

MatplotAlt: A Python Library for Adding Alt Text to Matplotlib Figures in Computational Notebooks.

Computer graphics forum : journal of the European Association for Computer Graphics·2026
Same journal

psudo: Exploring Multi-Channel Biomedical Image Data with Spatially and Perceptually Optimized Pseudocoloring.

Computer graphics forum : journal of the European Association for Computer Graphics·2025
Same journal

Are We There Yet? A Roadmap of Network Visualization from Surveys to Task Taxonomies.

Computer graphics forum : journal of the European Association for Computer Graphics·2024
Same journal

Visual Parameter Space Exploration in Time and Space.

Computer graphics forum : journal of the European Association for Computer Graphics·2024
Same journal

Doom or Deliciousness: Challenges and Opportunities for Visualization in the Age of Generative Models.

Computer graphics forum : journal of the European Association for Computer Graphics·2024
Same journal

Shape-Guided Mixed Metro Map Layout.

Computer graphics forum : journal of the European Association for Computer Graphics·2024
See all related articles

Related Experiment Video

Updated: Jun 30, 2025

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

2.5K

ParaDime: A Framework for Parametric Dimensionality Reduction.

Andreas Hinterreiter1, Christina Humer1, Bernhard Kainz2,3

  • 1Johannes Kepler University Linz Austria.

Computer Graphics Forum : Journal of the European Association for Computer Graphics
|March 20, 2024
PubMed
Summary
This summary is machine-generated.

ParaDime is a new framework for parametric dimensionality reduction (DR) that unifies popular methods like t-SNE and UMAP. It offers customizable tools for advanced high-dimensional data visualization and analysis.

Keywords:
CCS ConceptsInformation visualizationLearning latent representations• Computing methodologies → Neural networks• Human‐centered computing → Visualization systems and tools

More Related Videos

An Operant Intra-/Extra-dimensional Set-shift Task for Mice
08:35

An Operant Intra-/Extra-dimensional Set-shift Task for Mice

Published on: January 22, 2016

12.2K
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

6.9K

Related Experiment Videos

Last Updated: Jun 30, 2025

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

2.5K
An Operant Intra-/Extra-dimensional Set-shift Task for Mice
08:35

An Operant Intra-/Extra-dimensional Set-shift Task for Mice

Published on: January 22, 2016

12.2K
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

6.9K

Area of Science:

  • Computational science
  • Data science
  • Machine learning

Background:

  • Parametric dimensionality reduction (DR) uses neural networks to embed high-dimensional data into lower dimensions.
  • Existing DR techniques often stem from transformed inter-item relationships.

Purpose of the Study:

  • Introduce ParaDime, a unified framework for parametric DR.
  • Enable customization of DR processes for novel applications.

Main Methods:

  • ParaDime provides a common interface for specifying inter-item relationships and their transformations.
  • It integrates these into objective functions for neural network training.
  • Supports parametric versions of metric MDS, t-SNE, and UMAP.

Main Results:

  • ParaDime successfully unifies several parametric DR techniques.
  • Demonstrates suitability for hybrid classification/embedding models and supervised DR.
  • Facilitates experimentation with customized DR approaches.

Conclusions:

  • ParaDime offers a flexible and unified approach to parametric dimensionality reduction.
  • Enhances the exploration and visualization of high-dimensional data.
  • Opens new avenues for advanced data analysis and machine learning models.