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

Cluster Sampling Method01:20

Cluster Sampling Method

11.5K
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...
11.5K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

61
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,...
61
Scalar and Vector Triple Products01:06

Scalar and Vector Triple Products

2.2K
Two vectors can be multiplied using a scalar product or a vector product. The resultant of a scalar product is scalar, while with vector products, the resultant is a vector. These rules of the scalar or vector product between two vectors can be applied to multiple vectors to obtain meaningful combinations. The scalar triple product is the dot product of a vector with the cross product of two vectors.
The scalar triple product is the dot product of a vector with the cross product of two vectors....
2.2K
Aggregates Classification01:29

Aggregates Classification

289
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
289
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

4.4K
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...
4.4K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

82
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
82

You might also read

Related Articles

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

Sort by
Same author

Variance-constrained multi-view ensemble broad network for imbalanced data.

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

Learning to Super-Resolve Face Images via Dual-Domain Multi-scale Feature Interaction.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Effectiveness of heterologous mRNA vaccine boosters during an Omicron wave of COVID-19: a cross-sectional study in Macao (China).

Journal of thoracic disease·2026
Same author

Fast BCIs: Leveraging Dual-Scale Time Windows with Test-Time Adaptation to Enhance Accuracy.

IEEE transactions on bio-medical engineering·2026
Same author

FRE-GAN : Full-resolution efficient convolutional generative adversarial network for retinal vessel segmentation.

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

Riemannian Acceleration for Sparse PCA With Separable Structure and Second-Order Information Exploration.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

ASSR-Net: Anisotropic Structure-Aware and Spectrally Recalibrated Network for Hyperspectral Image Fusion.

IEEE transactions on neural networks and learning systems·2026
Same journal

PIMPC-GNN: Physics-Informed Multiphase Consensus Learning for Enhancing Imbalanced Node Classification in Graph Neural Networks.

IEEE transactions on neural networks and learning systems·2026
Same journal

Quantum Rényi α-Entropies for Graph Characterization.

IEEE transactions on neural networks and learning systems·2026
Same journal

LANet: A Lightweight and Accurate Balanced Network Based on State Space Models for Real-Time Semantic Segmentation.

IEEE transactions on neural networks and learning systems·2026
Same journal

MENDNet: Memory-Enhanced Dependency Network for Multistock Movement Prediction.

IEEE transactions on neural networks and learning systems·2026
Same journal

Temporal Mask-Embedding Learning and Query-Refined Head Network for Visual Tracking.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: May 10, 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

6.9K

Scale-Driven Tensor Representation-Based Multiview Clustering.

Chuanbin Zhang, Long Chen, Weiping Ding

    IEEE Transactions on Neural Networks and Learning Systems
    |April 24, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new framework for multiview clustering that unifies data into multiscale features. It effectively captures cluster shapes from coarse to fine levels, improving clustering accuracy for diverse data types.

    More Related Videos

    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    19.9K
    Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools
    07:58

    Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools

    Published on: November 11, 2020

    5.9K

    Related Experiment Videos

    Last Updated: May 10, 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

    6.9K
    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    19.9K
    Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools
    07:58

    Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools

    Published on: November 11, 2020

    5.9K

    Area of Science:

    • Data Science
    • Machine Learning
    • Computer Vision

    Background:

    • Real-world data often has hierarchical structures, offering natural multiview perspectives.
    • Existing multiview clustering methods typically ignore these hierarchical structures and focus on single-level relationships.
    • Current algorithms are designed for native multiview data, limiting their applicability.

    Purpose of the Study:

    • To develop a unified framework for multiview clustering applicable to diverse data types, including images.
    • To extract and leverage multiscale features for enhanced clustering performance.
    • To address the limitations of existing methods that overlook data hierarchy.

    Main Methods:

    • A novel scale-driven pre-processing approach unifies feature structures across data types.
    • Exploration of local sample relationships at multiple scales to capture both global and local details.
    • Learning view-specific partitions from different scales and deriving consensus features via tensor low-rank representation.

    Main Results:

    • The proposed method effectively captures precise cluster shapes from coarse to fine-grained levels.
    • Experimental comparisons show state-of-the-art (SOTA) performance against existing algorithms in multiview clustering and image segmentation.
    • The framework demonstrates versatility across diverse datasets.

    Conclusions:

    • The developed framework offers a comprehensive approach to multiview clustering by integrating hierarchical and multiscale feature extraction.
    • This method provides a unified way to cluster various data types, enhancing the understanding of complex data structures.
    • The approach successfully overcomes the limitations of traditional methods by exploiting data hierarchy.