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
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

1.7K
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...
1.7K
Multiple Bar Graph01:07

Multiple Bar Graph

5.0K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
5.0K
Deconvolution01:20

Deconvolution

127
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
127

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

Disease characteristics, treatment, and outcomes in Chinese chronic lymphocytic leukemia patients following BTK inhibitor discontinuation: a multicenter real-world study.

Frontiers in medicine·2026
Same author

Beef Cattle Behavior Recognition Based on Nighttime Farm Videos via Spatio-Temporal Enhancement and Dynamic Fusion.

Animals : an open access journal from MDPI·2026
Same author

Unsupervised feature selection via row-sparse local preserving projection.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

A Unified Framework for Pseudo-Supervised Clustering via Weighted Sample Aggregation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Projection with mixed-size anchor graphs.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

SimMTC: Simple Multi-View Tensor Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Granular Ball-Based Noise-Resistant Fuzzy Multineighborhood Feature Selection via Label Enhancement and Feature Graph.

IEEE transactions on neural networks and learning systems·2026
Same journal

Fighting Evolving Spam With ARTMAP Models: A Noise-Resilient Online Detection Framework.

IEEE transactions on neural networks and learning systems·2026
Same journal

HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

Negation of Basic Belief Assignment in Multisource Information Fusion on Dempster-Shafer Theory With Applications in Pattern Classification.

IEEE transactions on neural networks and learning systems·2026
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

IEEE transactions on neural networks and learning systems·2026
查看所有相关文章

相关实验视频

Updated: May 24, 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

11.3K

通过区块对角图选进行多视图集群.

Haonan Xin, Danyang Wu, Jitao Lu

    IEEE transactions on neural networks and learning systems
    |March 4, 2025
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了通过区块对角图选 (MvC-BDGF) 的多视图聚类来提高聚类准确性. MvC-BDGF学习自适应图形过器,增强特征可分离性,以获得更好的集群性能.

    更多相关视频

    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

    9.5K
    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

    相关实验视频

    Last Updated: May 24, 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

    11.3K
    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

    9.5K
    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

    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:

    • 基于图形的多视图集群方法利用特征和拓信息来提高准确性.
    • 现有的方法经常使用预先确定的图形过器,限制了对集群任务的适应性.
    • 在当前的方法中,过特征的分离能力较差,阻碍了有效的集群.

    研究的目的:

    • 提出一种新的多视图集群方法,可以学习集群友好的图形过器.
    • 为了解决预先确定的图形过器的局限性和现有方法中缺少的特征分离性.
    • 开发一个统一的框架,整合图表波器学习和共识图表获取.

    主要方法:

    • 通过区块对角图选 (MvC-BDGF) 引入了多视图集群.
    • 设计了一个具有局部特征的块对角图形过器,用于区分特征.
    • 开发了一个统一的框架,用于同时学习最佳过器和聚类标签.
    • 采用基于坐标下降方法进行优化的代解决程序.

    主要成果:

    • 拟议的MvC-BDGF模型有效地学习了集群友好的图形过器.
    • 集成过器学习与共识图获取以实现最佳集群.
    • 在基准数据集上实现了卓越的集群性能.
    • 通过广泛的实验证明了MvC-BDGF模型的有效性和优越性.

    结论:

    • 通过学习自适应图形过器,MvC-BDGF在多视图集群方面取得了重大进展.
    • 统一框架增强了特征歧视和聚类准确性.
    • 该方法为复杂的集群任务提供了强大的解决方案,性能优于现有的方法.