Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Multicompartment Models: Overview

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...

Cluster Sampling Method

Cluster Sampling Method

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...

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

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

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...

Weighted Mean

Weighted Mean

While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

Multiple Comparison Tests

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...

You might also read

Related Articles

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

Sort by

Same author

Fully unsupervised M-FISH chromosome image characterization.

IEEE journal of biomedical and health informatics·2013

Same author

Combining magnetic resonance spectroscopy and molecular genomics offers better accuracy in brain tumor typing and prediction of survival than either methodology alone.

International journal of oncology·2011

Same author

A Bayesian framework for image segmentation with spatially varying mixtures.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2010

Same author

The global kernel k-means algorithm for clustering in feature space.

IEEE transactions on neural networks·2009

Same author

Sparse Bayesian modeling with adaptive kernel learning.

IEEE transactions on neural networks·2009

Same author

Variational Bayesian sparse kernel-based blind image deconvolution with Student's-t priors.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2009

Search research articles

Related Experiment Video

Updated: Jun 8, 2026

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

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

Published on: February 15, 2017

Multiple view clustering using a weighted combination of exemplar-based mixture models.

Grigorios F Tzortzis¹, Aristidis C Likas

¹Department of Computer Science, University of Ioannina, Greece. gtzortzi@cs.uoi.gr

IEEE Transactions on Neural Networks

|October 12, 2010

Summary

This summary is machine-generated.

This study introduces a weighted multiview clustering method using convex mixture models (CMMs). It automatically assigns importance weights to different data views, improving clustering accuracy over unweighted approaches.

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Related Experiment Videos

Last Updated: Jun 8, 2026

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

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

Published on: February 15, 2017

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Area of Science:

Machine Learning
Data Mining
Pattern Recognition

Background:

Multiview clustering leverages multiple data representations for improved group assignments.
Existing methods often treat all views equally, risking poor performance with low-quality views.

Purpose of the Study:

To develop a multiview clustering algorithm that adaptively weights data views.
To address the limitation of equal view importance in standard multiview clustering.

Main Methods:

Proposes a weighted multiview convex mixture model (CMM) approach.
Involves training a CMM with automatically learned weights for each view.
Utilizes computationally efficient iterative computations.

Main Results:

Demonstrates the advantages of assigning weights to views in multiview clustering.
Shows superior performance compared to single-view CMMs and unweighted multiview CMMs.
Outperforms a kernel canonical correlation analysis-based multiview algorithm.

Conclusions:

The proposed weighted multiview CMM framework effectively improves clustering results.
Automatic view weighting is crucial for robust multiview clustering performance.
The method offers an efficient and implementable solution for complex datasets.