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

The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

7.8K
In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
7.8K
Anchoring Junctions01:03

Anchoring Junctions

5.4K
Anchoring junctions are multiprotein complexes that help cells connect to other cells and the extracellular matrix. Anchoring junctions are present on the lateral and basal surfaces of cells, providing strong and flexible connections. Focal adhesions are often formed due to cell interactions with the ECM substrata, which initiate signal transduction via kinase cascades and other mechanisms. Together, they provide stability and tissue integrity. There are three types of anchoring junctions:...
5.4K
Cluster Sampling Method01:20

Cluster Sampling Method

15.3K
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...
15.3K
Associative Learning01:27

Associative Learning

1.7K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
1.7K
Correlations02:20

Correlations

36.8K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
36.8K
Lipids as Anchors01:32

Lipids as Anchors

7.8K
In the plasma membrane, the lipids forming the bilayer can also act as an anchor to tether proteins to the membrane. The three main types of lipid anchors found in eukaryotes are – prenyl groups, fatty acyl groups, and glycosylphosphatidylinositol or GPI groups. Prenyl and fatty acyl groups act as anchors on the cytosolic surface of the membrane, whereas GPI anchors proteins on the extracellular side.
The carboxy-terminal of most of the prenylated proteins, such as Ras proteins, contains...
7.8K

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

Sublethal Concentration of Chloramphenicol Threatens the Health of <i>Bombus terrestris</i> by Regulating Gene Expression, Altering Enzyme Activity and Disrupting Gut Microbiota.

International journal of molecular sciences·2026
Same author

<b>Ontogeny of two gall-forming eriophyoid mites from Hainan Island, China (Acari: Eriophyoidea)</b>.

Zootaxa·2026
Same author

An oxygen-glucose co-releasing platform fostering dental pulp regeneration by driving metabolic recovery of stem cells.

Biomaterials·2026
Same author

Infrared and Visible Image Fusion Network Based on Self-Compensating Lightweight Convolution.

Sensors (Basel, Switzerland)·2026
Same author

Human CD24<sup>+</sup> dental papilla cells are competent seed cells for dentin-pulp regeneration via BMP2/SIRT1 axis.

Nature communications·2026
Same author

Advancing radiology foundation models with reasoning through step-by-step verification from daily reports.

Communications medicine·2026

相关实验视频

Updated: Mar 8, 2026

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

8.3K

通过图学习实现高阶相关性和一致性意识的多视图聚类.

Cheng Liang1, Wenchao Zang1, Daoyuan Wang2

  • 1School of Computer Science and Artificial Intelligence, Shandong Normal University, Jinan, 250358, Shandong, China.

Neural networks : the official journal of the International Neural Network Society
|March 6, 2026
PubMed
概括

通过图学习 (HCAGL) 的高顺序相关性和一致性意识多视图集群可以提高计算效率和准确性. 这种新的框架有效地捕捉了大规模异构数据中的高阶相关性和交叉视图一致性.

关键词:
安克拉图学习学习图表共识图表学习共识图表学习高阶的相关性是高阶的相关性.多视图聚类多视图聚类.

更多相关视频

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.6K
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

7.4K

相关实验视频

Last Updated: Mar 8, 2026

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

8.3K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.6K
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

7.4K

科学领域:

  • 机器学习 机器学习
  • 数据挖掘 数据挖掘
  • 人工智能的人工智能

背景情况:

  • 多视图集群集成来自多个数据源的信息.
  • 传统方法面临着计算效率,高阶相关性和交叉视图一致性方面的挑战,特别是对于大规模的异构数据.

研究的目的:

  • 提出一个新的框架,HCAGL,解决传统多视图集群的局限性.
  • 为了提高计算效率,捕捉高阶相关性,并保持交叉视图的一致性.

主要方法:

  • 通过一组紧的点来利用图学习来减少维度和提高效率.
  • 在低维嵌入上使用张量Schatten p-norm来捕捉高阶相关性并传播全球一致性.
  • 结合自适应社区图表学习来进行动态视图特定权重调整,以提高交叉视图的一致性.

主要成果:

  • 在六个基准数据集上,HCAGL在捕获交叉视图一致性和高阶相关性方面表现出卓越的表现.
  • 与现有的多视图方法相比,实现了更高的准确性和更好的集群质量.
  • 组件分析证实了设计选择的积极贡献,确保稳定可靠的结果.

结论:

  • HCAGL为复杂的多视图集群提供了有效和计算效率高的解决方案.
  • 该框架成功地解决了与大规模和异构的多视图数据相关的挑战.