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

Central Limit Theorem01:14

Central Limit Theorem

14.7K
The central limit theorem, abbreviated as clt, is one of the most powerful and useful ideas in all of statistics. The central limit theorem for sample means says that if you repeatedly draw samples of a given size and calculate their means, and create a histogram of those means, then the resulting histogram will tend to have an approximate normal bell shape. In other words, as sample sizes increase, the distribution of means follows the normal distribution more closely.
The sample size, n, that...
14.7K
Sample Size Calculation01:19

Sample Size Calculation

3.3K
Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
3.3K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.3K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.3K
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.5K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.5K
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

5.8K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
5.8K
Choosing Between z and t Distribution01:25

Choosing Between z and t Distribution

2.8K
The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
2.8K

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: Jul 2, 2025

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

15.8K

A Revised Formation of Trace Ratio LDA for Small Sample Size Problem.

Zhengxin Li, Feiping Nie, Rong Wang

    IEEE Transactions on Neural Networks and Learning Systems
    |February 21, 2024
    PubMed
    Summary

    This study introduces a revised trace ratio linear discriminant analysis (TR-LDA) to overcome the small sample size (SSS) problem, enhancing its applicability in dimensionality reduction for diverse datasets.

    More Related Videos

    Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
    06:48

    Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

    Published on: June 25, 2019

    9.2K
    Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
    06:16

    Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

    Published on: August 9, 2024

    408

    Related Experiment Videos

    Last Updated: Jul 2, 2025

    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

    15.8K
    Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
    06:48

    Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

    Published on: June 25, 2019

    9.2K
    Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
    06:16

    Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

    Published on: August 9, 2024

    408

    Area of Science:

    • Machine Learning
    • Data Science
    • Computer Vision

    Background:

    • Linear Discriminant Analysis (LDA) is a fundamental supervised dimensionality reduction technique.
    • Trace Ratio LDA (TR-LDA) is a well-established variant known for its clear interpretation.
    • TR-LDA faces convergence issues with small sample sizes (SSS) compared to data dimensions.

    Purpose of the Study:

    • To propose a revised TR-LDA formulation addressing the SSS problem.
    • To develop a novel optimization algorithm for the revised TR-LDA.
    • To provide theoretical insights into the SSS problem occurrence in TR-LDA.

    Main Methods:

    • A revised TR-LDA formulation designed for unified application across dataset sizes.
    • An optimization algorithm with convergence and complexity analysis.
    • Theoretical analysis using introduced theorems to define SSS problem conditions.

    Main Results:

    • The proposed TR-LDA method effectively overcomes the SSS problem.
    • The optimization algorithm demonstrates convergence and manageable computational complexity.
    • Experimental validation on real-world datasets confirms the method's effectiveness.

    Conclusions:

    • The revised TR-LDA offers a robust solution for dimensionality reduction, even with limited sample data.
    • The developed optimization algorithm ensures reliable performance and scalability.
    • This work expands the practical applicability of TR-LDA in machine learning.