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

Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

12.1K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
12.1K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

139
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,...
139
Multiple Bar Graph01:07

Multiple Bar Graph

5.1K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
5.1K
Inertia Tensor01:24

Inertia Tensor

459
The concept of the inertia tensor is employed to depict the mass distribution and rotational inertia of a solid or rigid object. This tensor is expressed through a three-by-three matrix. Each component within this matrix corresponds to varying moments of inertia about specific axes.
The diagonal components of the inertia tensor matrix represent the moments of inertia concerning the principal axes of the object. These primary axes are defined as the axes where the object experiences the least...
459
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

5.3K
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...
5.3K
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

2.4K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
2.4K

You might also read

Related Articles

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

Sort by
Same author

ITGA5 is overexpressed and promotes tumor progression through SNAI2 in OSCC.

Frontiers in cell and developmental biology·2026
Same author

An Efficient Regenerated Cross-Modal Hashing: Improving Existing Hash Codes with the Arbitrary Length.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Lactate-Induced Liquid-Liquid Phase Separation of HIF-1α Drives Myeloid Cell Polarization and Immune Evasion in Colorectal Cancer.

Cell proliferation·2026
Same author

Targeting phase separation: a new strategy to disrupt the stromal-immune axis in colorectal cancer.

Cell communication and signaling : CCS·2026
Same author

HFB301001, an OX40-based immunotherapy, drives Treg clearance and CTL activation through optimized OX40 receptor clustering.

Journal for immunotherapy of cancer·2026
Same author

Immunogenic tumor cell death and T-cell-derived IFN-γ elicit tumoricidal macrophages to potentiate OX40 immunotherapy.

Cell reports. Medicine·2026
Same journal

Dynamic analysis and reliable mechanical optimization application of ring HNN effected with a memristive neuron.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

DAFF-Net: A detection and search method for small-scale low surface brightness galaxies.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Quasi-synchronization for complex networks with hybrid pinning intermittent control.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Physics-encoded convolutional neural operators for parametric PDEs: A convergence-guaranteed framework via pre-computed kernel fields.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Exploiting audio-visual modalities in videos: Object detection via multi-stage bilateral coupling network.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles

Related Experiment Video

Updated: Jun 28, 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

Generalized latent multi-view clustering with tensorized bipartite graph.

Dongping Zhang1, Haonan Huang2, Qibin Zhao3

  • 1School of Automation, Guangdong University of Technology, Guangzhou 510006, China; Guangdong Key Laboratory of IoT Information Technology, Guangdong University of Technology, Guangzhou 510006, China.

Neural Networks : the Official Journal of the International Neural Network Society
|April 10, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces Generalized Latent Multi-View Clustering with Tensorized Bipartite Graph (GLMC-TBG), a novel method that uses neural networks to capture complex nonlinear data structures for improved multi-view clustering. GLMC-TBG outperforms existing algorithms on real-world datasets.

Keywords:
Latent representationMulti-view clusteringNeural networkNonlinear structure

More Related Videos

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.9K

Related Experiment Videos

Last Updated: Jun 28, 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
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.9K

Area of Science:

  • Machine Learning
  • Data Mining
  • Computer Vision

Background:

  • Tensor-based multi-view spectral clustering excels with high-order data correlations.
  • Existing methods struggle with nonlinear data structures due to linear consensus models.

Purpose of the Study:

  • To propose Generalized Latent Multi-View Clustering with Tensorized Bipartite Graph (GLMC-TBG).
  • To address limitations of linear models in capturing nonlinear data structures in multi-view clustering.

Main Methods:

  • Introduces neural networks for nonlinear mapping of graph structures into latent representations.
  • Employs nonlinear interactions for shared latent consensus across multiple views.
  • Utilizes an Augmented Lagrangian Multiplier with Alternating Direction Minimization (ALM-ADM) for optimization.

Main Results:

  • The proposed GLMC-TBG algorithm effectively captures nonlinear structures in complex data.
  • Achieves a more comprehensive common representation by integrating multiple views through nonlinear interactions.
  • Demonstrates superior performance compared to state-of-the-art algorithms across seven real-world datasets.

Conclusions:

  • GLMC-TBG offers a significant advancement in multi-view spectral clustering by incorporating nonlinear modeling.
  • The method provides a robust framework for uncovering complex patterns in multi-dimensional data.
  • Validated effectiveness on diverse real-world datasets, highlighting its practical applicability.