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

14.2K
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...
14.2K
Vesicular Tubular Clusters01:45

Vesicular Tubular Clusters

3.1K
After budding out from the ER membrane, some COPII vesicles lose their coat and fuse with one another to form larger vesicles and interconnected tubules called vesicular tubular clusters or VTCs. These clusters constitute a compartment at the ER-Golgi interface known as ERGIC (Endoplasmic Reticulum Golgi Intermediate Compartment). The ERGIC is a mobile membrane-bound cargo transport system that sorts proteins secreted from ER and delivers them to the Golgi.
With the help of motor proteins such...
3.1K
Lattice Centering and Coordination Number02:33

Lattice Centering and Coordination Number

11.4K
The structure of a crystalline solid, whether a metal or not, is best described by considering its simplest repeating unit, which is referred to as its unit cell. The unit cell consists of lattice points that represent the locations of atoms or ions. The entire structure then consists of this unit cell repeating in three dimensions. The three different types of unit cells present in the cubic lattice are illustrated in Figure 1.
Types of Unit Cells
Imagine taking a large number of identical...
11.4K
Paracrine Signaling01:21

Paracrine Signaling

59.5K
Paracrine signaling allows cells to communicate with their immediate neighbors via secretion of signaling molecules. Such a signal can only trigger a response in nearby target cells because the signal molecules degrade quickly or are inactivated if not taken up. Prominent examples of paracrine signaling include nitric oxide signaling in blood vessels, synaptic signaling of neurons, the blood clotting system, tissue repair/wound healing, and local allergic skin reactions. Nitric oxide as a...
59.5K
Intermolecular Forces03:13

Intermolecular Forces

70.6K
Atoms and molecules interact through bonds (or forces): intramolecular and intermolecular. The forces are electrostatic as they arise from interactions (attractive or repulsive) between charged species (permanent, partial, or temporary charges) and exist with varying strengths between ions, polar, nonpolar, and neutral molecules. The different types of intermolecular forces are ion–dipole, dipole–dipole, hydrogen bonds, and dispersion; among these, dipole–dipole, hydrogen...
70.6K
Network Covalent Solids02:18

Network Covalent Solids

16.1K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.1K

You might also read

Related Articles

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

Sort by
Same author

Difference in glucose tolerance between phytophagous and insectivorous bats.

Journal of comparative physiology. B, Biochemical, systemic, and environmental physiology·2019
Same author

Hybrid superconductor-atom quantum interface with Raman chirped shortcut to adiabatic passage.

Optics express·2019
Same author

GeTFEP: A general transfer free energy profile of transmembrane proteins.

Protein science : a publication of the Protein Society·2019
Same author

Plasma Homocysteine Level Is Associated with the Expanded Disability Status Scale in Neuromyelitis Optica Spectrum Disorder.

Neuroimmunomodulation·2019
Same author

Role of Plasma Calreticulin in the Prediction of Severity in Septic Patients.

Disease markers·2019
Same author

Mating yeast cells use an intrinsic polarity site to assemble a pheromone-gradient tracking machine.

The Journal of cell biology·2019

Related Experiment Video

Updated: Jan 25, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.6K

Subspace Clustering via Good Neighbors.

Jufeng Yang, Jie Liang, Kai Wang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |May 7, 2019
    PubMed
    Summary

    This study introduces a novel post-processing technique for subspace clustering in computer vision. By identifying "good neighbors," it enhances both sparsity and connectivity for improved high-dimensional data analysis.

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Data Science

    Background:

    • Spectral-based subspace clustering is key for high-dimensional data analysis.
    • Self-representation via affinity matrices is crucial for subspace clustering.
    • Optimizing sparsity and connectivity simultaneously is challenging.

    Purpose of the Study:

    • To propose a post-processing technique for optimizing sparsity and connectivity in subspace clustering.
    • To introduce the concept of 'good neighbors' for improved sample representation.
    • To enhance the performance of existing subspace clustering algorithms.

    Main Methods:

    • A post-processing method is proposed to refine the self-representation matrix.
    • The technique identifies 'good neighbors' with high affinity and mutual connectivity.

    More Related Videos

    Spatial Separation of Molecular Conformers and Clusters
    10:37

    Spatial Separation of Molecular Conformers and Clusters

    Published on: January 9, 2014

    11.7K
    CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis
    10:40

    CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis

    Published on: April 25, 2022

    2.8K

    Related Experiment Videos

    Last Updated: Jan 25, 2026

    JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
    07:28

    JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

    Published on: October 19, 2021

    3.6K
    Spatial Separation of Molecular Conformers and Clusters
    10:37

    Spatial Separation of Molecular Conformers and Clusters

    Published on: January 9, 2014

    11.7K
    CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis
    10:40

    CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis

    Published on: April 25, 2022

    2.8K

  • Coefficient reassignment and elimination are performed to generate a new coefficient matrix.

    • Main Results:

    • The 'good neighbors' approach effectively recovers subspace structures.
    • The post-processing step complements existing subspace clustering algorithms.
    • Experiments show favorable performance against state-of-the-art methods with low computational cost.

    Conclusions:

    • The proposed 'good neighbors' post-processing technique offers an effective way to improve subspace clustering.
    • This method enhances both sparsity and connectivity, addressing limitations of existing approaches.
    • It provides a computationally efficient and complementary addition to current subspace clustering frameworks.