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

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

613
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
613
Cluster Sampling Method01:20

Cluster Sampling Method

12.0K
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...
12.0K
Reduced Mass Coordinates: Isolated Two-body Problem01:12

Reduced Mass Coordinates: Isolated Two-body Problem

1.4K
In classical mechanics, the two-body problem is one of the fundamental problems describing the motion of two interacting bodies under gravity or any other central force. When considering the motion of two bodies, one of the most important concepts is the reduced mass coordinates, a quantity that allows the two-body problem to be solved like a single-body problem. In these circumstances, it is assumed that a single body with reduced mass revolves around another body fixed in a position with an...
1.4K
Multiple Regression01:25

Multiple Regression

3.1K
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...
3.1K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

88
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
88
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

1.5K
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...
1.5K

You might also read

Related Articles

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

Sort by
Same author

Embodied intelligence-driven adaptive collaboration in supply chains: A four-dimensional synergy framework and mechanism analysis.

PloS one·2026
Same author

ACR-SA: attention-based deep model through two-channel CNN and Bi-RNN for sentiment analysis.

PeerJ. Computer science·2022
Same author

A 3D-2D Convolutional Neural Network and Transfer Learning for Hyperspectral Image Classification.

Computational intelligence and neuroscience·2021
Same author

Pseudotext Injection and Advance Filtering of Low-Resource Corpus for Neural Machine Translation.

Computational intelligence and neuroscience·2021
Same journal

RETRACTION: Real-Time Modulation of Physical Training Intensity Based on Wavelet Recursive Fuzzy Neural Networks.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026
See all related articles

Related Experiment Video

Updated: Aug 5, 2025

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

7.0K

Localized Simple Multiple Kernel K-Means Clustering with Matrix-Induced Regularization.

Jiaji Qiu1, Huiying Xu1, Xinzhong Zhu1,2

  • 1School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China.

Computational Intelligence and Neuroscience
|March 27, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new multikernel clustering method, LI-SimpleMKKM-MR, enhancing data analysis by improving kernel weight flexibility and considering correlations between data instances for better clustering results.

More Related Videos

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ
08:59

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ

Published on: December 16, 2019

8.2K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

822

Related Experiment Videos

Last Updated: Aug 5, 2025

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

7.0K
Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ
08:59

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ

Published on: December 16, 2019

8.2K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

822

Area of Science:

  • Machine Learning
  • Data Mining
  • Pattern Recognition

Background:

  • Multikernel clustering handles linearly inseparable data using multiple views.
  • Localized SimpleMKKM (LI-SimpleMKKM) improves reliability by focusing on nearby samples.
  • Existing methods restrict kernel weights and ignore kernel matrix correlations.

Purpose of the Study:

  • To enhance multikernel clustering by addressing limitations of LI-SimpleMKKM.
  • To introduce a novel approach that removes kernel weight restrictions.
  • To improve the complementarity between base kernels and consider instance correlations.

Main Methods:

  • Proposed LI-SimpleMKKM-MR, incorporating matrix-induced regularization into LI-SimpleMKKM.
  • The regularization term enhances base kernel complementarity and considers paired instance correlations.
  • Evaluated performance on publicly available multikernel datasets.

Main Results:

  • LI-SimpleMKKM-MR demonstrated superior performance compared to existing methods.
  • The method effectively addresses kernel weight restrictions.
  • Enhanced consideration of correlations between paired instances improved clustering outcomes.

Conclusions:

  • LI-SimpleMKKM-MR offers an improved approach to multikernel clustering.
  • The matrix-induced regularization effectively enhances clustering performance.
  • The method provides a more flexible and comprehensive way to analyze multikernel data.