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

Ogive Graph01:07

Ogive Graph

6.7K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
6.7K
Graphing Antiderivatives01:30

Graphing Antiderivatives

49
The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...
49
Bar Graph01:07

Bar Graph

21.5K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
21.5K
Time-Series Graph00:54

Time-Series Graph

5.0K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
5.0K
Multiple Bar Graph01:07

Multiple Bar Graph

9.0K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
9.0K
Dimensional Analysis03:40

Dimensional Analysis

60.5K
Dimensional analysis, also known as the factor label method, is a versatile approach for mathematical operations. The main principle behind this approach is: the units of quantities must be subjected to the same mathematical operations as their associated numbers. This method can be applied to computations ranging from simple unit conversions to more complex and multi-step calculations involving several different quantities and their units.
Conversion Factors and Dimensional Analysis
The unit...
60.5K

You might also read

Related Articles

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

Sort by
Same author

Non-sebaceous lymphadenoma of the salivary gland: case report with immunohistochemical investigation.

Virchows Archiv : an international journal of pathology·2007
Same author

[Enhancement of HSP-MUC1 antitumor activity by type C CpG-ODN BW005].

Xi bao yu fen zi mian yi xue za zhi = Chinese journal of cellular and molecular immunology·2007
Same author

Inhibition of severe acute respiratory syndrome-associated coronavirus infection by equine neutralizing antibody in golden Syrian hamsters.

Viral immunology·2007
Same author

Synergistic effect between components of mixtures of cationic amphipaths in transfection of primary endothelial cells.

Molecular pharmaceutics·2007
Same author

The insertion polymorphism in angiotensin-converting enzyme gene associated with the APOE epsilon 4 allele increases the risk of late-onset Alzheimer disease.

Journal of molecular neuroscience : MN·2007
Same author

[Study on the analysis of mixed spectra of benzene homologs with Dolittle multivariate correction method].

Guang pu xue yu guang pu fen xi = Guang pu·2007

Related Experiment Video

Updated: Jan 23, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

2.1K

Learning Low-Dimensional Latent Graph Structures: A Density Estimation Approach.

Li Wang, Ren-Cang Li

    IEEE Transactions on Neural Networks and Learning Systems
    |June 22, 2019
    PubMed
    Summary

    This study introduces novel methods for learning latent graph structures from high-dimensional data. These techniques enhance feature extraction and selection for better data representation and visualization.

    More Related Videos

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
    12:18

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

    Published on: January 11, 2020

    7.9K
    Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
    08:58

    Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

    Published on: November 19, 2018

    13.0K

    Related Experiment Videos

    Last Updated: Jan 23, 2026

    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
    07:35

    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

    Published on: October 13, 2023

    2.1K
    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
    12:18

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

    Published on: January 11, 2020

    7.9K
    Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
    08:58

    Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

    Published on: November 19, 2018

    13.0K

    Area of Science:

    • Machine Learning
    • Data Science
    • Graph Theory

    Background:

    • High-dimensional data presents challenges in representation and analysis.
    • Unsupervised learning requires effective methods for feature extraction and selection.
    • Learning latent graph structures can reveal underlying data relationships.

    Purpose of the Study:

    • To develop a unified framework for density estimation to learn latent graph structures.
    • To propose novel methods for feature extraction and feature selection in unsupervised learning.
    • To uncover compact and informative representations of high-dimensional data.

    Main Methods:

    • A unified density estimation framework for simultaneous feature extraction and selection.
    • A feature extraction method integrating discriminative information with structure learning.
    • A feature selection method preserving pairwise distances on optimal features.

    Main Results:

    • The proposed methods achieve competitive or superior quantitative results in discriminant evaluation.
    • Learned embeddings reveal smooth skeleton structures of data.
    • Optimal features are selected, unveiling correct graph structures.

    Conclusions:

    • The developed methods effectively learn latent graph structures from high-dimensional, unsupervised data.
    • The approach provides a compact and informative data representation.
    • The methods facilitate visualization and accurate structure discovery.