Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Cluster Sampling Method01:20

Cluster Sampling Method

11.6K
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...
11.6K
Aggregates Classification01:29

Aggregates Classification

305
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...
305
In- and Out-Groups01:31

In- and Out-Groups

38.9K
People all belong to a gender, race, age, and social economic group. These groups provide a powerful source of our identity and self-esteem (Tajfel & Turner, 1979) and serve as our in-groups. An in-group is a group that we identify with or see ourselves as belonging to.
38.9K
Self-Discrepancy Theory02:45

Self-Discrepancy Theory

18.3K
One influential perspective on what motivates people's behavior is detailed in Tory Higgin's self-discrepancy theory (Higgins, 1987). He proposed that people hold disagreeing internal representations of themselves that lead to different emotional states.  
18.3K
Group Polarization01:01

Group Polarization

34.2K
Group polarization is the strengthening of an original group attitude following the discussion of views within a group (Teger & Pruitt, 1967). That is, if a group initially favors a viewpoint, after discussion the group consensus is likely a stronger endorsement of the viewpoint. Conversely, if the group was initially opposed to a viewpoint, group discussion would likely lead to stronger opposition.
34.2K
Stereotype Content Model02:16

Stereotype Content Model

14.0K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
14.0K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

SeOMLR: one-step multi-view latent representation with self-weighted ensemble learning for multi-omics cancer subtyping.

Bioinformatics (Oxford, England)·2026
Same author

Structure-enhanced graph meta learning for few-shot gene regulatory network inference.

Genome biology·2025
Same author

TransGRN: A Transfer Learning-Based Framework for Inferring Gene Regulatory Networks Across Cell Lines.

IEEE journal of biomedical and health informatics·2025
Same author

LGFFM: A Localized and Globalized Frequency Fusion Model for Ultrasound Image Segmentation.

IEEE transactions on medical imaging·2025
Same author

LineGRN: A Line Graph Neural Network for Gene Regulatory Network Inference.

IEEE journal of biomedical and health informatics·2025
Same author

Unveiling spatial domains from spatial multi-omics data using dual-graph regularized ensemble learning.

Communications biology·2025

相关实验视频

Updated: Jun 9, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.4K

格雷斯 (GRESS):基于信念的集群深度对比的子空间集群.

Yujie Chen, Wenhui Wu, Le Ou-Yang

    IEEE transactions on cybernetics
    |October 22, 2024
    PubMed
    概括

    我们介绍了GRESS,这是一种新的深次空间聚类方法,通过整合聚类信息和更高阶关系来增强自我表达系数,以提高数据分析的精度.

    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 基于自我表达的子空间聚类严重依赖于自我表达系数.
    • 提高这个系数的精度对于准确的集群结果至关重要.

    研究的目的:

    • 提出一种新的深次空间聚类方法,GRESS,以提高自我表达系数的精度.
    • 将聚类信息和高阶关系集成到系数矩阵中.

    主要方法:

    • 开发了一个深度对比的子空间集群模块,用于同时学习系数和集群表示.
    • 引入了基于信念的聚类亲和力改进模块,以利用更高层次的关系.
    • 实现了杂的相似性抑制和相似性增量规范化.

    主要成果:

    • 实现了无声的自我表达和基于集群的相似性.
    • 在相似性矩阵中有效地发现了更高阶的关系.
    • 在基准数据集上表现出比最先进的方法更优异的性能.

    结论:

    • 通过改进自我表达系数,GRESS显著提高了子空间聚类的准确性.
    • 集群信念和对比学习的整合提供了一个强大的方法来发现复杂的数据关系.

    更多相关视频

    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
    Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
    07:34

    Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

    Published on: June 3, 2013

    17.3K

    相关实验视频

    Last Updated: Jun 9, 2025

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    2.4K
    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
    Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
    07:34

    Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

    Published on: June 3, 2013

    17.3K