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

11.9K
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
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

108
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
108
Associative Learning01:27

Associative Learning

407
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...
407
Multiple Comparison Tests01:13

Multiple Comparison Tests

3.9K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
3.9K
Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

137
The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
137
Phase Contrast and Differential Interference Contrast Microscopy01:26

Phase Contrast and Differential Interference Contrast Microscopy

8.1K
Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...
8.1K

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

Ribonucleic acid binding protein-mediated regulation of luteinizing hormone receptor expression in granulosa cells: relationship to sterol metabolism.

Molecular endocrinology (Baltimore, Md.)·2007
Same author

Psychological stress-induced oxidative stress as a model of sub-healthy condition and the effect of TCM.

Evidence-based complementary and alternative medicine : eCAM·2007
Same author

Overexpression of OsCOIN, a putative cold inducible zinc finger protein, increased tolerance to chilling, salt and drought, and enhanced proline level in rice.

Planta·2007
Same author

Edge-based scoring and searching method for identifying condition-responsive protein-protein interaction sub-network.

Bioinformatics (Oxford, England)·2007
Same author

[The value of long-term postoperative follow-up after curative resection of lung cancer and common problems associated with it].

Nihon Geka Gakkai zasshi·2007
Same author

Identification of a type III thioesterase reveals the function of an operon crucial for Mtb virulence.

Chemistry & biology·2007

相关实验视频

Updated: Jul 10, 2025

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

对于未配对的多视图集群的选择性对比学习.

Like Xin, Wanqi Yang, Lei Wang

    IEEE transactions on neural networks and learning systems
    |November 23, 2023
    PubMed
    概括

    本研究介绍了对不配对的多视图集群 (UMC) 的选择性对比学习. 该方法即使没有配对的样本,也有效地集群数据,提高了联合集群的性能.

    科学领域:

    • 机器学习 机器学习
    • 数据挖掘 数据挖掘
    • 计算机视觉 计算机视觉

    背景情况:

    • 未配对的多视图集群 (UMC) 是一个新兴的挑战,在这个挑战中,在视图中无法获得配对的样本.
    • 现有的不完整的多视图集群方法依赖于样本配对,这对于UMC来说是无效的.
    • 有效的联合聚类需要在多个视图中利用未配对的样本.

    研究的目的:

    • 解决未配对多视图集群 (UMC) 的新问题.
    • 提出一种方法,在没有配对数据的视图之间挖掘一致的集群结构.
    • 克服UMC中不确定的集群结构和不确定的集群配对的挑战.

    主要方法:

    • 为UMC开发了选择性对比学习 (scl-UMC).
    • 引入了内视 (IV) 选择性对比学习模块,通过选择自信样本来增强聚类结构.
    • 设计了一个交叉视图 (CV) 选择性对比学习模块,用于代的集群匹配和收紧,通过相互信息来增强.

    主要成果:

    • 拟议的scl-UMC方法在未配对的多视图集群中显示出显著的效率.
    • 实验结果显示,与现有最先进的方法相比,性能优越.
    • 该方法成功地解决了无监督集群结构不确定性和不确定的集群配对的挑战.

    更多相关视频

    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    20.0K
    Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
    07:12

    Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

    Published on: April 11, 2025

    381

    相关实验视频

    Last Updated: Jul 10, 2025

    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
    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    20.0K
    Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
    07:12

    Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

    Published on: April 11, 2025

    381

    结论:

    • 选择性对比学习为未配对的多视图集群提供了有效的解决方案.
    • 拟议的内部视图和交叉视图对比学习模块增强了聚类的准确性和稳定性.
    • 这项工作推动了多视图聚类领域的发展,通过使用未配对数据实现联合聚类.