Jove
Visualize
Contact Us

Related Concept Videos

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

354
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,...
354
Cluster Sampling Method01:20

Cluster Sampling Method

13.7K
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...
13.7K
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.8K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
4.8K
Gaussian Elimination: Problem Solving01:30

Gaussian Elimination: Problem Solving

61
Systems of linear equations in several variables are pivotal in modeling complex scenarios involving multiple unknowns and constraints. Such systems are widely used in various fields to represent relationships where several conditions must be simultaneously satisfied. Each variable in the system corresponds to an unknown quantity, while each equation imposes a linear constraint, leading to a structured approach for analyzing and solving real-world problems.A system of three equations with three...
61
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

257
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...
257
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

6.1K
It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
6.1K

You might also read

Related Articles

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

Sort by
Same author

Evaluating Tumor Burden as a Predictive Biomarker for Epidermal Growth Factor Receptor Targeted Kinase Inhibitor Therapy in Advanced Non-Small Cell Lung Cancer.

JCO precision oncology·2026
Same author

Leveraging large language models to extract smoking history from clinical notes for lung cancer surveillance.

NPJ digital medicine·2025
Same author

Colorectal cancer relies on an immunosuppressive cellular topography and genomic adaptations for establishing brain metastases.

bioRxiv : the preprint server for biology·2025
Same author

Automatic Abstraction of Computed Tomography Imaging Indication Using Natural Language Processing for Evaluation of Surveillance Patterns in Long-Term Lung Cancer Survivors.

JCO clinical cancer informatics·2025
Same author

Correction: The performance status gap in immunotherapy for frail patients with advanced non-small cell lung cancer.

Cancer immunology, immunotherapy : CII·2024
Same author

Multi-Task Learning and Sparse Discriminant Canonical Correlation Analysis for Identification of Diagnosis-Specific Genotype-Phenotype Association.

IEEE/ACM transactions on computational biology and bioinformatics·2024
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 Experiment Video

Updated: Nov 16, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.2K

Multi-Manifold Optimization for Multi-View Subspace Clustering.

Aparajita Khan, Pradipta Maji

    IEEE Transactions on Neural Networks and Learning Systems
    |February 19, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel manifold optimization algorithm for multi-view data clustering. The method effectively identifies consensus clusters by integrating information from individual data views, outperforming existing approaches.

    More Related Videos

    Basics of Multivariate Analysis in Neuroimaging Data
    06:35

    Basics of Multivariate Analysis in Neuroimaging Data

    Published on: July 24, 2010

    17.1K
    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

    7.2K

    Related Experiment Videos

    Last Updated: Nov 16, 2025

    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    20.2K
    Basics of Multivariate Analysis in Neuroimaging Data
    06:35

    Basics of Multivariate Analysis in Neuroimaging Data

    Published on: July 24, 2010

    17.1K
    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

    7.2K

    Area of Science:

    • Computational Biology
    • Data Science
    • Machine Learning

    Background:

    • High-dimensional multi-view datasets contain complex patterns often represented in lower-dimensional manifolds.
    • Effective identification of hidden data structures relies on accurately modeling the geometry of these low-dimensional manifolds.

    Purpose of the Study:

    • To present a novel manifold optimization-based integrative clustering algorithm specifically designed for multi-view data.
    • To identify consensus clusters by constructing a joint graph Laplacian that denoises and integrates information from individual views.

    Main Methods:

    • The algorithm optimizes a joint clustering objective while minimizing discrepancies between joint and individual view cluster structures.
    • Optimization is performed alternately on k-means and Stiefel manifolds to model nonlinearities and find joint cluster structures.
    • Gradient-based movements are applied to each view's manifold to preserve individual nonlinearities while seeking shared cluster information.

    Main Results:

    • The algorithm's convergence is mathematically established over the manifold, with an asymptotic convergence bound derived.
    • Evaluations on benchmark and multi-omics cancer datasets show superior performance compared to state-of-the-art multi-view clustering methods.

    Conclusions:

    • The proposed manifold optimization approach provides an effective strategy for integrative clustering of multi-view data.
    • This method demonstrates significant improvements in identifying consensus clusters, particularly in complex biological datasets.