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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

480
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...
480
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

5.2K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
5.2K
Cluster Sampling Method01:20

Cluster Sampling Method

15.5K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
15.5K
Variability: Analysis01:11

Variability: Analysis

629
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
629
Multiple Regression01:25

Multiple Regression

4.3K
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...
4.3K
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

5.6K
In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
5.6K

You might also read

Related Articles

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

Sort by
Same author

Copper-cobalt peroxide nanoparticles: a biomimetic cascade reaction for enhanced Fenton-like therapy at physiologically relevant pH.

Nanoscale·2024
Same author

Key target genes related to anti-breast cancer activity of ATRA: A network pharmacology, molecular docking and experimental investigation.

Heliyon·2024
Same author

Synthesis of imidazolium ionic liquid immobilized on magnetic mesoporous silica: A sorbent material in a green micro-solid phase extraction of multiclass pesticides in water.

Talanta·2024
Same author

Hollow mesoporous silica nanoparticles: Effective silica etching using tri-di- and mono-valent cations.

Biomaterials advances·2022
Same author

Virtual Screening on Marine Natural Products for Discovering TMPRSS2 Inhibitors.

Frontiers in chemistry·2021
Same author

Chemical compositions and experimental and computational modeling activity of sea cucumber Holothuria parva ethanolic extract against herpes simplex virus type 1.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie·2021

Related Experiment Video

Updated: Mar 28, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K

Variable selection in multivariate calibration based on clustering of variable concept.

Maryam Farrokhnia1, Sadegh Karimi2

  • 1The Persian Gulf Marine Biotechnology Research Center, Bushehr University of Medical Sciences, Bushehr, Iran.

Analytica Chimica Acta
|December 26, 2015
PubMed
Summary

A new variable selection algorithm, clustering of variable (CLoVA), was adapted for regression problems. CLoVA-PLS demonstrated superior prediction performance compared to conventional methods, identifying informative variables for stable models.

Keywords:
Clustering of variable – partial least squareInterval based partial least squarePartial least squareSelf organization mapVariable selection

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.4K

Related Experiment Videos

Last Updated: Mar 28, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.4K

Area of Science:

  • Chemometrics
  • Machine Learning
  • Data Analysis

Background:

  • Variable selection is crucial for building robust predictive models.
  • Traditional methods may not effectively handle complex spectral data.
  • Clustering of variable concept (CLoVA) was previously developed for classification.

Purpose of the Study:

  • To adapt the CLoVA algorithm for regression problems.
  • To evaluate the performance of CLoVA-based variable selection in PLS regression.
  • To introduce synergy clustering of variable (sCLoVA-PLS) as an enhanced approach.

Main Methods:

  • Clustering algorithms were used to group spectral variables.
  • Partial Least Squares (PLS) regression was applied to clustered spectral data.
  • CLoVA and sCLoVA-PLS were compared against conventional variable selection strategies.

Main Results:

  • CLoVA-PLS consistently outperformed other methods in prediction accuracy across diverse datasets.
  • Variable clustering effectively separated relevant from redundant spectral information.
  • sCLoVA-PLS offered an efficient modification, leading to stable model development.

Conclusions:

  • CLoVA-PLS is a superior variable selection strategy for regression tasks involving spectral data.
  • Variable clustering enhances PLS model stability by focusing on informative features.
  • sCLoVA-PLS represents an effective advancement for predictive modeling.