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

Fischer Projections02:18

Fischer Projections

13.2K
Learning to draw Fischer projections of molecules and understanding their relevance plays a crucial role in the visual depiction of organic molecules. A Fischer projection is a two-dimensional projection on a planar surface to simplify the three-dimensional wedge–dash representation of molecules. This is especially helpful in the case of molecules with multiple chiral centers that can be difficult to draw. Here, all the bonds of interest are represented as horizontal or vertical lines.
13.2K
Cluster Sampling Method01:20

Cluster Sampling Method

11.9K
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...
11.9K
Random Sampling Method01:09

Random Sampling Method

11.0K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures 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. Among the various sampling methods used by...
11.0K
Newman Projections02:06

Newman Projections

16.8K
Different notations are used to represent the three-dimensional structure of molecules on two-dimensional surfaces. One of the most commonly used representations is the dash-wedge formula. The dashed wedges, solid wedges, and the plane lines indicate the groups situated behind the plane, coming out of the plane, and in the plane, respectively.
The organic molecules rotate across the single bonds leading to numerous temporary three-dimensional structures of varying energy known as...
16.8K

You might also read

Related Articles

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

Sort by
Same author

DR1-DR2 enrichment in HBeAg-negative chronic hepatitis B: integration hotspot or survivor signature?

Gut·2026
Same author

Impact of chronic obstructive pulmonary disease on the prognosis of patients with extensive-stage small-cell lung cancer treated with chemoimmunotherapy.

Translational lung cancer research·2026
Same author

Emergent Dynamical Kondo Coherence and Competing Magnetic Order in a Correlated Kagome Flat-Band Metal CsCr_{6}Sb_{6}.

Physical review letters·2026
Same author

Timeliness gap in non‑EPI Childhood vaccination in urban China: a five-city study of socioeconomic determinants and caregiver preferences.

International journal for equity in health·2026
Same author

Intrinsic Benefits of Categorical Distributional Loss: Uncertainty-aware Regularized Exploration in Reinforcement Learning.

Advances in neural information processing systems·2026
Same author

Phenylpropanoids from <i>Rhodiola fastigiata</i> (Hook.f. & Thomson) Fu (Crassulaceae) and their antiplasmodial activities.

Natural product research·2026

Related Experiment Video

Updated: Jun 25, 2025

Sample Drift Correction Following 4D Confocal Time-lapse Imaging
10:04

Sample Drift Correction Following 4D Confocal Time-lapse Imaging

Published on: April 12, 2014

16.4K

Fast Fusion Clustering via Double Random Projection.

Hongni Wang1, Na Li1, Yanqiu Zhou2

  • 1School of Statistics and Mathematics, Shandong University of Finance and Economics, Jinan 250014, China.

Entropy (Basel, Switzerland)
|May 24, 2024
PubMed
Summary

This study introduces faster, more accurate fusion clustering algorithms using random projection alternating direction method of multipliers (ADMM) for high-dimensional data. These novel methods improve computational speed and clustering precision over traditional approaches.

Keywords:
ADMM algorithmfusion clusteringrandom projectionunsupervised learning

More Related Videos

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

15.7K
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

Related Experiment Videos

Last Updated: Jun 25, 2025

Sample Drift Correction Following 4D Confocal Time-lapse Imaging
10:04

Sample Drift Correction Following 4D Confocal Time-lapse Imaging

Published on: April 12, 2014

16.4K
High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

15.7K
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

Area of Science:

  • Machine Learning
  • Data Science
  • Computational Statistics

Background:

  • Clustering is fundamental in unsupervised learning for data processing.
  • Fusion clustering offers improved stability and accuracy over k-means and hierarchical methods.
  • Traditional fusion clustering optimization faces computational burdens due to complex fusion penalties.

Purpose of the Study:

  • To develop a computationally efficient and accurate algorithm for high-dimensional fusion clustering.
  • To address the limitations of existing optimization methods in fusion clustering.
  • To introduce random projection techniques to accelerate the ADMM algorithm.

Main Methods:

  • Introduced a random projection ADMM algorithm utilizing Bernoulli distribution.
  • Developed a double random projection ADMM method for high-dimensional fusion clustering.
  • Evaluated algorithm convergence and performance on simulated and real-world datasets.

Main Results:

  • The proposed random projection ADMM methods significantly enhance computational speed.
  • These methods demonstrate improved clustering accuracy compared to the classical ADMM algorithm.
  • The double random projection approach further boosts performance in high-dimensional settings.

Conclusions:

  • Random projection ADMM offers a superior alternative for high-dimensional fusion clustering.
  • The new algorithms provide a balance of speed and accuracy for complex datasets.
  • The study validates the effectiveness and convergence of the proposed methods.