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

Stereotypes, Prejudice, and Discrimination02:55

Stereotypes, Prejudice, and Discrimination

95.0K
Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who...
95.0K
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

52
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...
52
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

You might also read

Related Articles

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

Sort by
Same author

Hierarchical feature based dual contrastive multiview clustering.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Unsupervised feature selection via row-sparse local preserving projection.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

A Unified Framework for Pseudo-Supervised Clustering via Weighted Sample Aggregation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Projection with mixed-size anchor graphs.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

SimMTC: Simple Multi-View Tensor Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Unsupervised fine-tuning of vision-language models by fusing classifier tuning and visual prompt tuning.

Neural networks : the official journal of the International Neural Network Society·2026

Related Experiment Video

Updated: Jan 25, 2026

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
04:57

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

17.4K

Submanifold-Preserving Discriminant Analysis With an Auto-Optimized Graph.

Feiping Nie, Zheng Wang, Rong Wang

    IEEE Transactions on Cybernetics
    |April 30, 2019
    PubMed
    Summary

    This study introduces a new local dimensionality reduction (DR) framework for complex, non-Gaussian data. The auto-optimized graph embedding method enhances robustness and recognition accuracy by preserving local structures and handling noise effectively.

    More Related Videos

    Orthotopic Kidney Auto-Transplantation in a Porcine Model Using 24 Hours Organ Preservation And Continuous Telemetry
    07:58

    Orthotopic Kidney Auto-Transplantation in a Porcine Model Using 24 Hours Organ Preservation And Continuous Telemetry

    Published on: August 21, 2020

    7.8K
    Evaluating Skilled Prehension in Mice Using an Auto-Trainer
    05:01

    Evaluating Skilled Prehension in Mice Using an Auto-Trainer

    Published on: September 12, 2019

    6.0K

    Related Experiment Videos

    Last Updated: Jan 25, 2026

    Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
    04:57

    Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

    Published on: May 16, 2022

    17.4K
    Orthotopic Kidney Auto-Transplantation in a Porcine Model Using 24 Hours Organ Preservation And Continuous Telemetry
    07:58

    Orthotopic Kidney Auto-Transplantation in a Porcine Model Using 24 Hours Organ Preservation And Continuous Telemetry

    Published on: August 21, 2020

    7.8K
    Evaluating Skilled Prehension in Mice Using an Auto-Trainer
    05:01

    Evaluating Skilled Prehension in Mice Using an Auto-Trainer

    Published on: September 12, 2019

    6.0K

    Area of Science:

    • Machine Learning
    • Data Science
    • Computer Vision

    Background:

    • Traditional dimensionality reduction (DR) methods struggle with multimodal, non-Gaussian data.
    • Existing techniques like Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) fail to preserve global structures effectively in complex datasets.
    • The multimodality and non-Gaussian nature of data present significant challenges for conventional DR approaches.

    Purpose of the Study:

    • To propose a novel local DR framework using auto-optimized graph embedding for extracting intrinsic submanifold structures in multimodal data.
    • To develop a method that preserves local neighborhood structures while effectively handling noise and redundant features in high-dimensional data.
    • To introduce a robust and accurate DR solution for complex datasets where traditional methods fall short.

    Main Methods:

    • A k-nearest neighbors (kNNs) graph is constructed to preserve local neighborhood structures.
    • The model employs l0-norm and binary constraints on the similarity matrix, ensuring k-connectivity.
    • A key innovation is the simultaneous learning of the embedding space and similarity matrix, enabling auto-optimized neighbor selection in an optimal subspace.

    Main Results:

    • The proposed framework demonstrates improved robustness and recognition accuracy compared to traditional methods.
    • Experiments on synthetic and real-world datasets validate the effectiveness of the auto-optimized graph embedding approach.
    • Four supervised and semisupervised local DR methods derived from the framework successfully extract discriminative features while preserving submanifold structures.

    Conclusions:

    • The novel local DR framework effectively addresses the limitations of traditional methods for non-Gaussian, multimodal data.
    • Simultaneous optimization of the embedding space and similarity matrix, coupled with an efficient iterative algorithm, overcomes NP-hard challenges.
    • The method significantly enhances robustness and recognition accuracy, offering a powerful tool for complex data analysis.