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

12.0K
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...
12.0K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

242
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
242
Stratified Sampling Method01:16

Stratified Sampling Method

12.1K
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. 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.
To choose a stratified sample, divide the population into groups called strata and then take a...
12.1K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.7K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.7K
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

2.5K
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...
2.5K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

112
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
112

You might also read

Related Articles

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

Sort by
Same author

[Experimental study of the eyelid reconstruction in situ with the acellular xenogeneic dermal matrix].

Zhonghua zheng xing wai ke za zhi = Zhonghua zhengxing waike zazhi = Chinese journal of plastic surgery·2007
Same author

[Mutation analysis of GCH1 gene in Chinese patients with dopa responsive dystonia].

Zhonghua yi xue yi chuan xue za zhi = Zhonghua yixue yichuanxue zazhi = Chinese journal of medical genetics·2007
Same author

[Screening and characterization of marine bacteria with antibacterial and cytotoxic activities, and existence of PKS I and NRPS genes in bioactive strains].

Wei sheng wu xue bao = Acta microbiologica Sinica·2007
Same author

[Collateral supply in patients with severe carotid stenosis].

Zhonghua yi xue za zhi·2007
Same author

[Changes of sleep architecture in patients with narcolepsy].

Zhonghua yi xue za zhi·2007
Same author

[Combined anterior and posterior approach for cervical fracture-dislocation with ankylosing spondylitis].

Zhonghua wai ke za zhi [Chinese journal of surgery]·2007

Related Experiment Video

Updated: Jul 21, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.5K

Low-rank discrete multi-view spectral clustering.

Yu Yun1, Jing Li1, Quanxue Gao1

  • 1School of Telecommunications Engineering, Xidian University, Shaanxi 710071, China.

Neural Networks : the Official Journal of the International Neural Network Society
|July 26, 2023
PubMed
Summary

This study introduces a novel low-rank discrete multi-view spectral clustering model. It effectively utilizes complementary information from different views for improved clustering performance.

Keywords:
Discrete label learningLow-rankSpectral clustering

More Related Videos

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
14:58

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Published on: June 2, 2010

9.6K
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: Jul 21, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.5K
Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
14:58

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Published on: June 2, 2010

9.6K
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:

  • Computer Science
  • Data Science
  • Machine Learning

Background:

  • Spectral clustering is popular for its ability to handle complex data structures.
  • Existing multi-view spectral clustering methods often overlook complementary information and use suboptimal discrete solutions.

Purpose of the Study:

  • To propose a novel low-rank discrete multi-view spectral clustering model.
  • To address limitations in existing methods by exploiting complementary information and integrating discrete label recovery.

Main Methods:

  • Developed a model using tensor Schatten p-norm to exploit complementary information from indicator matrices.
  • Integrated low-rank tensor learning and discrete label recovery into a unified framework.
  • Avoided the uncertainty of traditional relax-and-discrete strategies.

Main Results:

  • The proposed model effectively leverages complementary information across multiple views.
  • The integrated framework provides a more direct and optimal discrete solution.
  • Experimental results on benchmark datasets validate the method's effectiveness.

Conclusions:

  • The proposed low-rank discrete multi-view spectral clustering model offers superior performance.
  • This approach enhances clustering by utilizing complementary information and direct discrete optimization.